{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import efinance as ef\n",
    "import pandas as pd\n",
    "import numpy as py\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入qstock模块\n",
    "import qstock as qs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_NOTEBOOK\n",
    "import qstock as qs\n",
    "from qstock import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['etf09.xls',\n",
       " 'etf10.xls',\n",
       " 'etf13.xls',\n",
       " 'etf14.xls',\n",
       " 'etf15.xls',\n",
       " 'etf16.xls',\n",
       " 'etf17.xls']"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 指定文件夹路径\n",
    "folder_path = r'C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\实测数据'\n",
    "\n",
    "# 获取文件夹下的所有文件\n",
    "files = os.listdir(folder_path)\n",
    "\n",
    "files\n",
    "# data = pd.read_csv(r'C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\实测数据\\etf09.xls')\n",
    "# data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     time    thscode   tradeDate tradeTime  latest        amt  \\\n",
      "0     2023-11-09 09:30:00  513130.SH  2023-11-09  09:30:00   0.550   615060.0   \n",
      "1     2023-11-09 09:30:03  513130.SH  2023-11-09  09:30:03   0.550   647686.3   \n",
      "2     2023-11-09 09:30:06  513130.SH  2023-11-09  09:30:06   0.551  1161076.8   \n",
      "3     2023-11-09 09:30:09  513130.SH  2023-11-09  09:30:09   0.550  1483085.3   \n",
      "4     2023-11-09 09:30:12  513130.SH  2023-11-09  09:30:12   0.551  6517866.4   \n",
      "...                   ...        ...         ...       ...     ...        ...   \n",
      "6588  2023-11-17 14:59:47  513130.SH  2023-11-17  14:59:47   0.543   254100.0   \n",
      "6589  2023-11-17 14:59:50  513130.SH  2023-11-17  14:59:50   0.543   101000.0   \n",
      "6590  2023-11-17 14:59:53  513130.SH  2023-11-17  14:59:53   0.543  1782800.0   \n",
      "6591  2023-11-17 14:59:56  513130.SH  2023-11-17  14:59:56   0.543   919600.0   \n",
      "6592  2023-11-17 14:59:59  513130.SH  2023-11-17  14:59:59   0.543  1592300.0   \n",
      "\n",
      "             vol  tradeNum  \n",
      "0      1117200.0     126.0  \n",
      "1      1177200.0     145.0  \n",
      "2      2107800.0     168.0  \n",
      "3      2694800.0     212.0  \n",
      "4     11848800.0     333.0  \n",
      "...          ...       ...  \n",
      "6588    468700.0   36642.0  \n",
      "6589    186100.0   36652.0  \n",
      "6590   3283500.0   36676.0  \n",
      "6591   1694000.0   36702.0  \n",
      "6592   2933400.0   36745.0  \n",
      "\n",
      "[45460 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "all_data_to_gf = pd.DataFrame()\n",
    "data = pd.DataFrame()\n",
    "for i in files:\n",
    "    data = pd.concat([data,pd.read_csv(rf'C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\实测数据\\{i}')],axis=0)\n",
    "    # print(data)\n",
    "\n",
    "for i in data[',time,thscode,tradeDate,tradeTime,latest,amt,vol,tradeNum']:\n",
    "    # 原始文本数据\n",
    "    raw_data = i\n",
    "\n",
    "    # 通过 StringIO 创建一个类文件对象，用于读取数据\n",
    "    data_stream = StringIO(raw_data)\n",
    "\n",
    "    # 读取数据并设置列名\n",
    "    df = pd.read_csv(data_stream, header=None, names=['time', 'thscode', 'tradeDate', 'tradeTime', 'latest', 'amt', 'vol', 'tradeNum'])\n",
    "    all_data_to_gf = pd.concat([all_data_to_gf,df],axis=0)\n",
    "    # 打印数据框\n",
    "print(all_data_to_gf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_to_gf.to_excel('all_data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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6+w9TOqy3pGcoZ36hkicWBNQ+ACA8WHo7AAAAAAAAJGlIcq5GpOZpW+X3csj35OYz56eqMSFWJpmkvYfX5952p+JHjaaHJgBEET7RAQAAAABhY97YWTIZhl91GxNiJalD/YQxY0loAkCU4VMdAAAAABA2shMydfnY2QG1EWh9AED4I6kJAAAAAAgrBQPyddW4S2QxzC1Dyb1kMcz60bhLVDAgvwejAwCEA+6pCQAAAAAIO8cNyJfVHKNXN76hWi/r3H3CT5SdkNmjcQEAwgNJTQAAAABAWCmpO6DlG17TlsrtMhne99QkoQkAfQdJTQAAAABA2Ph8X5Fe2rBSDqdTkuRw+j4LOgAg+nFPTQAAAABAWPh8X5FeWP+KbE67HPI9mfn5vqIeiAoAEI5IagIAAAAAel1JXale2rAyoDZe2rBSJXUHghQRACCckdQEAAAAEFQOm01lq97R/uV/UNmqd+Sw2Xo7JESA5Rtedw0595fD6dTyDa91WL/l5kJtv+dnOvjWP3g/AkCU4J6aAAAAAIKm5LVXVbHqbalNcurAayuUNm2GsmfN6cXIEM52VhVrS+V2j9uG72rQN16245BDWyq3a+vy/ydp2OH1NTVy1NTo4B9X6uAfVypt+kzejwAQ4eipCQAAACAoSl57VRXvvOWW0JQkOZ2qeOctlbz2au8EhrD36b41Hmc5H76rQWf9u8qntn5QVCPbPz/usgzvRwCIfCQ1AQAAAATMYbO19NCUZJdJXyUPV4Ul0a1Mxaq3GfoLj7ZUbO8wy7nhcOoHa+v00IgrvG7HZHMof0Ndh/UvD5ymKnOC2zrejwAQ2UhqAgAAAAhYxQfvu3pofpkyUv/of4reHHCqeyGns6Uc0M6+2pIO6waWNuv9lB/41M74LfUySTLard+ZMEBP513svpL3IwBENJKaAAAAAALWXHo4KbU5abAkaU9cVpflAElyOB2yO+0d1ifWO7Q5aYjHOmZHx/Jxx76n1OrDvT2v2vm3bvfN+xEAIhcTBQEAAAAIWExWtuux0cUM1m3LAZJkMkwyG+YOic3aeM99cH665UW35denpml3f6skqTL5cJ3+TWXd7pv3IwBELnpqAgAAAAhY2pSpktE66LeTpKZhtJQD2hmQ2DG5uCcrpss6TknVCSa3cl+NiJfj0Lb2Q9A74P0IABGNpCYAAACAgJksFqVNmyGp82RS2rQZMlkYLIaORqTldZj93GnqPC3Zmjb/aGKSWzmHxaSisQmeK7XD+xEAIhtJTQAAAABBkT1rjtKmz5TRvqemYSht+kxlz5rTO4Eh7J2YU9Bh9vOu1CSY9Pcfpmjr4LgO2z7OT5LltK4nGOL9CACRj5+lAAAAAARN9qw5StKX0taDkqTM2ZcobcpUesShS0OSczUiNU/bKr+XQ10nN1+fmqY9WTEee3KaZNKw1CM0fMo1csy5SnrkX27b+100W+lnTOP9CABRgJ6aAAAAAILKaJNsypg2nQQSvDJv7CyZjG7vhKnd/a2dDk03GYbmjZ3V8tjD+67fzDN5PwJAlCCpCQAAACCoDC8SU0B72QmZunzs7IDauHzsbGUnZAYpIgBAOOMnKgAAAABBRUoT/ioYkC+npJc2rJTD6ey2vNQy5NxkGLp87GwVDMjv2QABAGGDpCYAAACAoKKjJgJx3IB8HZEyWMs3vKavuyhnMkxyOB0alnaE5o2ZRQ9NAOhjSGoCAAAACC6ymghQdkKmbpk4X//z7gcdtpkNs3IS+2t42lCdmFOgIcm5vRAhAKC3kdQEAAAAEFSdzOECBMWTp/2yt0MAAIQBJgoCAAAAEFRMFAQAAHoaPTUREIfNpooP3ldzaYlM6elqLilRw+aNMswWpZx0itLPmCaThbcZAABAX0JKE11xOhyq/upLHXzzDdlrquUwDOngQc+FR1wR0H7aq/7qSyUddbQME/17IpHT4VD9po2yVVbKnJysxupqHXj+t5KtWZJkSk/X4J/fp9iUlF6OFEAokG2C30pee1UVq96WOpmV8OAfV+rgH1cqbfpMZc+aE+LoAAAA0FvoqInOVK9do73PPC15SDj2yH6GXea2fu+Tj0tms3Kum6/kiQU9GgOCq3rtGpWuWC5beXmnZRzl5fr+1ptkxMdr5FPLQhgdgN7Az1PwS8lrr6rinbc6TWi2VfHOWyp57dUQRAUAAIDwQFYTHVWvXaO9y5YGnNDctOC6wPZjt2vvsqWqXrsmoDgQOq2vaVcJzbac9fXafOP8Ho4KQG8jqQmfOWy2lh6aPqhY9bYcNlsPRQQAAIBwQk9NtOd0OLT3pd8Hp7GmJjVUVAS8n5JXlnscoo7w4nQ4VLpiue/16uvVWFXVAxEBCBcMP4fPyt9/z62H5n/Sj1alJUk2k1nrk4dJkjIby3XNrr8eruR0quKD95UxbXqowwUAAECIkdREe/WbNkrV1a7lL5NH6K3+J0uSZu7/xPXYW8X336MRjz3Z7X66Yq8oV/2mjUoYM9anfSO06jdtdOuhuSrzeMU7GvXDsi/1buZxWpt2+PW7aM8HGllX7FoufuA+DX/o0ZDGCyB06KkJnzWVlLgt/7tfvr5KHelKaErSgdj0DvWaS0s6rAMAAED0SUmwuh43Ntt7MRKEC1tlpdty2ySmrwlNSXLU1Xm1H1/jQvhp+xodiEnVF2lj9J+MYyTJLaEpSX8cOMVt2V5T0/MBAug19NSEz6zZ2X7Vi8nyrx4AAAAiS1pyrOuxw9H9PdgR/SypqQG3Ma56u2aUrJYkmRISgrKfYMSFntX2NbIZZp/qmpOSgh0OgDBCT034LH3q6b6PKTIMpU2Z2jMBAQAAIKy0PVP0Yl5J9AHxo0ZLyckBtXHu/n/L6my5T3/uPfcHvB9zWnpLeYS1+FGjZUnvOBLQG7l33xvkaACEE5Ka8JnJYlHatBk+1UmbNkMmCx2DAQAA+gLD7QdwspqQDJNJOZddGZzGrFbFpaUFvJ/sS+bJMHFJHO4Mk0lZc+f5Xi8+XrEpKT0QEYBwwSc4/JI9a47Sps/0qsdm2vSZyp41JwRRAQAAIByY2pwiMvocrZInFihnfqEUSCLRatWop58NbD9ms3LmFyp5YoH/cSCkWl9TS4p3vXCN+HiNfGpZD0cFoLfRdQ5+y541R5kXXCQ98i+P2/tdNFvpZ0yjhyYAAEBf0+aHbyfjz9FG8sQCJf3m/0kPf+hz3SGPPNFpD01v95Nz0y1KOupoemhGoOSJBRo4cKT0+7WSpEG33i69ub9DuSMee5IemkAfwSc5AmKYO79Rc7+ZZ5LQBAAA6IMYfI6u+JtQ9Dah2dV+kscfQ0IzghnG4dcucdyRHsuQ0AT6Dj7NERB+eAcAAEBXOF8EAAA9gaQmAuLkt3cAAAB0geHnAACgJ5DUREA4RwUAAEBXOF8E0BP4wQQASU0EhO8RAAAAdIXEAwAA6AkkNREgTlIBAADQOXKaAACgJ5DUREA4SQUAAEBX6KkJoCfwyQKApCYCwjkqAAAAusLpIgAA6AmW3g4Akcve1KTil5ZLOsLj9k03XC9zYpJSJ5+mjGkzZLLwdgMAAOgL7Dab6/H3Tzym8qYqqWS/e6GEBJksFhkxVqVOmtzj54vNdXXa/cSjspWUyJSQoMw5lyj56PEyTCY11dRoxwP3SaWlLYVNJik+XmpokOx2yTBkJCUr7fQz1G/6zKg/r3U6HKr+6kvte+1Vaf++wxtiYqSkZKm6WrI1t6wzm1v+RoceJx5/ojJnzdHu556RbcO3ne9kxBU99wS6sOnG+Uo6tkCJY8YoJj1D8aNGyzDR1yfcOR0OVX6xVt+v+IuUOUWStPnaH3l8H21eeIOcDqeM+Hg5m5qkmuqWDYYhU0qqko6ZoKy5l8pstYbyKQDoAWHzbXzgwAHdc889Wr16tfLy8vSLX/xCY8aM6bbexo0bde6557qtmzp1qp5++mlJ0h/+8ActXrzYbftdd92lq666SpJ04403atWqVW7b33jjDY0dOzaAZxP9ipcuUd26IjUaFmm456SmGhtlb2xU2Z9eV9mfXlfa9JnKnjUntIECAAAgpIqXLtGBbbVS9omSJNv+fVJzdceCdXVyHHrY0+eL2+66Q7bSEteyo6Za+556QvtMJhmxsXLW17tXcDik2trDy06nnNVVKv/zH1X+5z9G9Xlt9do12vvM0y1/g/aam6XyMvd1rQnNQ49rV/9Htav/07NBBqK+XjX/+bdq/vNvSZIlPV1Zc+cpeWJBLweGzlSvXaO9y5a2LFgzXOudMjyWdx46dp31de02OOWorFDVvz5U1b8+VMKEfOUWLuyRmAGERlgkNZ1OpwoLCyVJr7/+ur766istWLBAf/3rX5WYmNhl3aKiIp100kl68sknXessbX45LSoqUmFhoa688krXuri4OLftzz//vI466ijXuu722dftXPK46tYVSZKchucvEk8q3nlLkqL2BBAAAKCva/3hWymjXOs6Szx40hPni+0Tmm4cjo4JTS9E63mtW/IoCmy59SYp+/wuy9jKy1ue8/xCEpthqCffk3XrilS8dAmJTSCChUVS84svvlBRUZH+/ve/a/jw4Ro+fLj+9re/6b333tN5553XZd2ioiKdcMIJSklJ6XT7vHnzPG7ftWuXamtrVVBQICtdz71ir29UTVGRa9lmdP4W2hmXrdyGUpna3EmpYtXbyrzgoqgfsgMAANDX2JuaXD98t9VoilGjEaNqS4IaTTFqMFuVbKtTvL1RNZYEZTZVKMZ5uLdfMM8Xm+vq3BKateY41Zti1a+5skOqtc4UqwPWVMU47Yq3Nyje3qTKmEQ1GxbZTGYNqd/vVifazmudDof2vvR7t3UHYlJVGZOoGIddJjk0sOGA6sxxshlmNZpi1GyyyOR0uP4ucfZGpdtqJLX8rSssSbIbZjkMQ8ahawKT0ym70fPDvZtqauSoqpKy3dfbZchpGGoyYpTgaHStL13xspLyj2UoehhxOhza++LzbutKYtMPP7amt68iSaoyJ8hpGLIbJqU3V2tPXKYcMinGaVNmY4UsOtwLuW5dkexNTQxFByJUWHwDr1+/XgMHDtSIESNc6/Lz8/Xll192m9T84osv9M033+jZZ59VbGysZs6cqTvvvFNxcXHat2+f9uzZo/vvv1/btm1Tdna2rrzySl1xxRWuuhaLRWeddZb27dunkSNH6vbbb9dJJ50U0POxWKLzi9BsNmn77w+f6NhlaGnerE7Lv5w7Q5L00y0vHl7pdKr6ow/Ub/qMHosTgO/MZpPb/wCiB8c3QqX05Vc9rn9x8Fnd1r1168uyOg/dhzOI54s7n3zc9XhH/ACtGDTNtdz2HLXUmqbfDnG/pZUn4XZeG8zju3bDxpZ7ZR7yRcporco+wed2Ti/9TKNrvtevu7hO8Jcv11nbH/6lx/W/GnG56/FFez7QyLpiSZKtvExNWzcrkduQhY3aDRvdbgOxJG+26s2HR12+MORsj/Wezru4y3bv2PIHt443Za+/qpwrruyiRu/g+xvoXlgkNaurqzVkyBC3dampqdqwYUOX9SoqKlRTU6PCwkKdfvrp2rVrl2677TbFx8frjjvu0HfffachQ4bo1ltv1dixY/XJJ59o0aJFOuKIIzRp0iRt3bpVY8eO1e23366srCy98MIL+vGPf6x//vOfysjI6HLfnTGZDKWnR+/w9Z1797oeN5hj/WrDqCyL6r8REMlSUuJ7OwQAPYTjGz1td1mp33X3xWZoSMPhHpXBOl+0Vxx0PX4n68ROy33Y71i/2g+X89pgHN82m/swfH8SmpL0XtbxajAFv9fbb3461ae/taO6QpI0+cBafZg50WOZt7NP0sgdr7mWY231YfF6okX792TbhGYg6sxxSrIfbttRVhrWrzvf30DnwiKpabFYFBvrniCLi4tTXV1dJzVapKWlafXq1a7l/v37q7CwUE888YTuuOMOTZ48WZMnT3ZtP//88/XJJ5/oL3/5iyZNmqRbb73Vrb0777xTq1at0qpVqzR37ly/novD4VRVVddxRyqz2aS4nBxVrvtKktr8tuUbZ2qGystruy8IIGTMZpNSUuJVVVUvu93DxAAAIhbHN0LFlJEVQG33weDBOl80p/VT84GWiW38PXftSm+f1wbz+G60BC9x4st999uyOGy6bdvLruX0KVPdetD58rc2JafJXl2rEyu+1VHVW7U0b3bHONstN1riuU4JI768J4+u2qKvU0Z0X9ADU0ZWWL7ufH+jL0tJifeql3JYJDXT09N14MABt3U1NTV+3ecyOztbpaWlamxs7JAobd3+2WefeaxrGIYyMzNVXFzs837bstmi9wMn78ortf+tdyT5dtN3F8NQ8qQpUf03AiKZ3e7g+ASiFMc3elrGxXNU/sH7gTcUxPPFnJtu0fabFrjaDaowOq8NxvFtHT5SSk52G4LuL7+uEyTXfTdbZVw8x+/nNeiOu7Tj5sJD7XrWNvlqSc+QdfjIsHg90cI6fKSUmOg2BL0zJqf/P1sE8j4LBb6/gc6Fxc0ZJkyYoM2bN6uqqsq17uuvv1ZOTk6X9T766CPdcMMNbuvWrl2rfv36KTY2Vk899ZSeffbZDttb2/3Rj36kNWvWuLZVV1dr8+bN3e63LzPHxyopP1+SfycradNmRM3N1AEAAHCY2WpVwoTW80TfONucVgbzfDEmIUGWrGy/YupOtJ3XGiaTci4Ln/sKJkzID2jyFmtSkkyHJos1Okl4tb2eyZp7KZMEhRnDZFLOFT/yrqz8S/oF+j4D0LvC4lN75MiRGjZsmB577DE5HA59++23WrVqlaZMmSKHw6GqqirZ7fYO9Y488kitXr1azz77rL777jstX75czz//vC6/vOXmz+PHj9ezzz6rd955R99++60WL16sdevW6bLLLpMkjRs3Tvfdd58+//xzrV27VjfeeKPi4+N11lnd38y8Lxuy8BYlTMj3eVhJ2vSZyp41p4eiAgAAQG/LLVzoSmz6oyfOF4f98mFZsrI9/yBvMsmI933YdbSe1yZPLFDO/EIpwOSevwnk1p6aCRPylVu4MKAYJGnEY0/KlJLSZVcMS3qGcuYXKnliQcD7Q/C53pPd8KenZrDeZwB6j+F0BtBPO4jWr1+v66+/Xs3NzaqurtZ5552nX/ziFyouLtbUqVP1xhtvaKyHmeg+++wzLV68WNu2bdOgQYN09dVXa9asWTIOJdyWL1+uZ555RhUVFRo3bpx+8pOf6LjjjpMkNTU16YEHHtDf//53OZ1OnXLKKbrjjjuUm5vr9/Ow2x0qKwu/+3EEg8ViUnp6osrLa2WzOVRyoEo//X9ruq338Pg6ZUTZL9lAtGl/fAOIHhzf6A3vf/69lr+/1evy1w1r0PEXTuvR88WfLP1Y5TVNruUnZmYo+ejxMkwmPf7KWn39fWW3bTx0dK36TZ8ZNue1PXV8Ox0OXf3wh37Xn54/QO8U7fO5Xqzh0NJbTg16z7nyAxX6yf/7osP6eIuhpbdOoodmBOjuPXlifKU+rU/1qq2f9i/W8Hlzw76HJt/f6MsyMhIj556aUkuvyXfeeUeff/650tPTNX78eElSbm6uNm7c2Gm9448/Xm+++Wan2+fNm6d58+Z53Ga1WnXffffpvvvuCyz4PsqwxHhVLvPMs3s4EgAAAIQTk9nsU/nU40/s8UShYXLvr5dyzITD27zcd9ZZ5wQzpLAVaJLPFOtfsshktfZIoik2yfPM1k6TiYRmhOjudUo56ijp811etZU9a07YJzQBeCdskpqSlJCQoEmTJvV2GPBSmHTyBQAAALrFqWsI+fm3DvJUTofb7eS2WbwloofJh1uj8VkARA9+loLf+C4AAABAMITivNJBJiNk/P1TB3uC+m7b5S0RlhxO34dam0w9lRIHEM7CqqcmIgvnhQAAAAiGkJxWdrET0iHB9eneNZKyfa7XWY/KQBXX7PG4vsnerJWb3tCJOQUakuz/vAoIzM7qYn26d422VGzXvtoS2Z12mQ2zBiRma0Ranlevjy93EWDEIRA9SGrCb3wZAAAAIFJ0debKWW1w1TSHx8SpJXUHtHzDa9pc/r2kaR22O51O/Xv3p/qo+BONSM3TvLGzlJ2QGfpA+6jW12dL5XaZDJNbD027067dNXu1t3a/6/WRRnfali/Dz+m1DUQPkprwm4PvAgAAAHjg82liKM4rSWSEjNPpX4/LYI4g/nxfkV7asPJQAquz195wJdK2VX6vB/77qC4fO1sFA/KDFwg8cn99Oh9y3vb1CVZSk48CIHqQ1IT/+DYAAABAEDhDkNXkB/kIEKTh55/vK9IL619p23C3dRxyyOGUnl//ipySjiOx2WM6vj7dc6jr+2z68tbhowCIHkwUBL+R0wQAAEA0cJDxDAvBSGmW1JXqpQ0rvWu4k5f9pQ0rVVJ3IAjRoD2Pr08Q+HI/Vm6jBkQPemrCZ811ddrxq4dVXForDZjebfm67zYoftRoGb7cvRkAAAB9RvHjj8pat0eyxMjIy5P27JazqUnmlBTl3n2vYlNSfG6zsapK39/3c6myUpJky5sjmWNd2zdd8yNJTslkUm3OVCk+p9s2N11zlecNhiFzerpSJ09RxrQZMll69zLLYbPp4Kq3VfHxv+Us2X94Q4xVScefoOxL5qlhy2aVffwvNaz/Vqqr69jIiCv8D8DP4efB6Ki5fMPrHu6Z2EkSy2no2pUl+mJMnIrGJclhablecTidWr7hNd0ycX7gAcGN59fHnbnJrh98Wau08kalVTiVaGvpjfWrEV1Ucng/Y/r2n92pyuZqSZIpNU1Jx0xQxoUXa++rr6hx9X+6rhwfr/TpM9Vvxpm9fpwDIKkJH629foEa9h06MbKme1Wn+JGHZElPV9bceUqeWNCD0QEAACAcNOzc6V9FW7Ocmze5Fu0HD+r7W2+SER+vkU8t87qZzTfOl7O+3m1dxzTKoTUOR+A9t5xO2cvKVPan11X2p9eVNn2msmfNCaxNP5W89qoq3nnL88bmJtX859+q+c+/QxuUlwKd/XxnVbG2VG73ZYdKsEk/+KZBp3zToLVj4/Wf/GQ55NCWyu3aWV3MrOhB5M3rc9ZHFRq+u8nnXrvlb/xRypzoVVlnm9YdlRWq+teHqvrXh97tqL5e5W/8SeVv/KlXj3MALeg6B69tvuO2wwlNSU4fTjps5eXau2ypqteu6YnQAAAAECaq165R1b8+Cmqbzvp6bb7Ru15znhKaLTo/d3UGZeDzYRXvvKWS114Napve6DKhGVLB/Xt669N9a2QyOl7ienvZMnFDvU4pOtSDzzDp071cuwRTZ69Pq9aEpn+8/2EiWMd7bx3nAA6jpya80lxXp+aSEtfyB/0m6rP0I72qW2OOU5K9QZJUuuJlJeUfy1B0AACAKOR0OLT3Dy9IpkE+1Vs58HRJ0rzit5XWXK1f582SJN28bYXiHC1JDmd9vRqrqrocit5YVeVKaDolPXPEBaqISVZac7UazVa3sg8Nv0xWh63D+q482MWQ7Av2/lOja3e5litWva3MCy4K2RBVh83mltDcET9AKwZNcytzctlXOrVsnSSpypygv/c/RXXmOJXGejcCy1v2kiF+1Qt09vMtFds9zqJtrW+SpzS3JK1LGakJVZtlqOU9c+yGeq0+OlEOi7S1YkdgAcGNp9fHUZOixvUnS5Jej5d+kL5OmU0VMjsd+iplhHIbSvTPzO5H+5l86G39/444z/V47u53NbR+ryTp+/j+emVQ17dXm7n/Ex1TvcW1HOrjHIA7Mkvwyp4lj7keV1oSvU5oStJvh5zremwrL1P9po1BjQ0AAADhoX7TRqmmxu/6y3NnuBKakvTEsLlu24sfuK/L+m23b0gaqoqYZEly/d+W0zD5lNDszp9zTpOjbQ8wp1MVH7wftPa7035f7ROakvRJxnhVm+MlSU/nXazvE3KCntAMxNihGQHV31db4nH9xe9XdVrn7eyTXI8NtVwgj9/SkgLdW7vfcyX4xdPr05rQbPVxvwl6I2ey/jhwijYnDfEqoSlJqTb/PndWDDrD9bi7hKYkvdXfPd5QH+cA3JHUhFdsZWWux1WWxC7LZjaWuy3Xm+Pc2zp0s3YAAABEl9bzPD/niemWvZuEadvt2xMG9kwQXWg/rLW51HOSrSd4u69aS3wPR+K/K6aP9ruuw+mQ3Wn3uC2x3qH+DQe9biu1uqU3od1p99jzE77r6vUJ1LDaYg2vLe6Rtr0RyuMcgDv6SMMrlowM2cvLui330y0vuh53NjzHkpoatLgAAAAQPro7z5tW8qlWZZ/od/vmpKRut9sbGyX5coe9w1rPZR8ZdqlsppZLpR/v+JN+M/RCr+q332dMVrYfUfinJ/f10y0vejy3jz3yEzV+e7KHGofFH/92t+2bDbOePO2XfscntdwD02yYPSbOauNNunjvB269gLtSmWxyxdXVPSDhva5eH19NqNykGaWfuq3r7njv7D0cDKE8zgG44xMaXhm48NagtGNJz1D8KP9/gQUAAED4ih81Wuoi8RhoB87cu+/1ensgk4EYbVIkhl/pUUmGobQpU/2OwVde7yvAid57Qk5i/6C0MyDRc3Lp9amd34e1Lackh6SvRsQHNS606Oz18Vfbt3LvTE2lkB/nANyR1IRXYhISFJMd+JdQ1txLmSQIAAAgShkmk3Iuv6rz7QFk1Iz4+C4nCZKk2JQUGfGHhld7O+W1p325PfYh5jYV06bNCOnkISaLRWnTZ4Zsfy0Cz5CaDJOGpw0NPBRJI9LyPPasbIq3qi626/dD6zP5Ymy8HBZTUONCi85eH3+0vl6u472XhPo4B+CO7BK8NvLhRxQ3wLtfK41Y9/toWtIzlDO/UMkTvbvRMwAAACJT8sQCpf5wUlDbNOLjNfKpZV6VHfnUMhnx8f731LRaZbSZSdmXWZVb95k2faayZ83xb/8ByJ41p9vEZiCJ5Y6NBd6Ww+nQiTnBuUY4Maeg03tgvnJm95MQrR0br//kJwc9LrTo6vXxh2nwII18apkSJuQHrU1f9NZxDuAwflKATyY+87Tq/7tVWvmNx+2Zsy9R2pSpLb0xH/7QtT7voUfooQkAANBHxA4eIn23qcP6xPxjpd2+tXXEY09220OzvZFPLVP860XSlvLuC7eR9+TTiklIkPnxj6TGlnv/JZ5wouTlPCAZ512g/jN6t+dW9qw5yrzgIumRf3ncnjDhWA27Ypb0xCdetzn44cekZ9d1WG8KsI+MSSYNSz1CQ5JzA2qn1ZDkXI1IzdO2yu/lkPfJs4+PilPRuCQ5LKYeiQst/H192qtKMOkfP5qoW065UZKUW7hQ9qYm6bGPO62TPOm0Tj97Uk6drIwLL5aWfubV/tPPv1D9ZpxJD00gDJBlgs8scbGdbsuYNl0mi6VDApOEJgAAAFLGH+NzHV8Tmq0MS4zPdWISElpru9YNnHuJ1/Uzps8Mi0RHVzFknXe+LHFxnW73JD7Dcy9HI4Ah/pJkMgzNG+vd5D3emjd2lkw+xrV2fIorodlTcaGFP69Pe7sGxWlevvtxabZau6yTc/mVnW4bcMVVsnYzCVlbWWefGxbHOQCSmgAAAABCJYSzeTh9GDbeXtuci0+JuzCchKe9QBORbZ0zbHpA9S8fO1vZCZlBiqZFdkKmLh87u+MGH552T8SFFp2+Pj4YmTaM1weAJJKaAAAAAELECGFWM4Ccpltd33Ka4Z/VDOYrcGS/MT7XMckki2HWj8ZdooIBPXMvxIIB+bpq3CWyGGavh8iHIi60aPv6+IOEJoBW9JmGH0L4EzsAAACiRhA7CXYrkJ6abfmSiA3SLqOSyTDJ4XRoWNoRmjdmVo8npo4bkK8jUgZr+YbXtKVye7ezbocqLrRofX1++t+vejsUABGMpCb8wNkaAAAAfBfapKb/dd2HnwceS1gJ8fMxG2blJPbX8LShOjGnIKST72QnZOqWifO1s7pYH21fq392Uu7O425iUqBeQAIZQKBIagIAAAAIiWDez7E7jmD11PRl+HkE/PYf6hztk6f9MsR77GhIcq7OG56tf8rz7NgkNAEgMnFPTQAAAAAhEcqEWvCSmr5EHQlZzWjreuqlPvq0ASCa0VMTPrHXN+rA229Jyvap3p7nfiNrbq4cdfWyl5fJktFPCWPHKWH0GBkmcusAAAB9gh+JpX0vvais2XNltlo7LdNYVaXv7/mZVFPjWlc7eLoU29+fKN1EWUpT39/3c9XWl0kjrujtUMLGpuuvlhEXr6Rjj1X2JZe5vdeaamq068HFsu/bd7iCNVZJxx2nuEGDlTZlqkwWLqvbcthsKn37H6p8b5VUWytZLLKkpcs6cpRsFeVq+m6D5HC0FA6D9+Gma65qeeBlLPampi4/jwCEDp++8NrOJY+rpqhINXHZUu6MLsuWvPaqpCzXcs1/P5X+616m/B9/kykxSf2vuErJEwt6IGIAAACEE5MfvQSrPvxAVR9+oIQJ+cotXNhh++Yb58tZX99hvdNu9yvG9nzpqRkJw89lC87fJdIUP/YryXKS5412u5y1Nar+979U/e9/ud5rW269SY6qqo7lmxpV85+PVSPpwGsrlDZthrJnzenR+CNFyWuvquKdt9xXNjfLVloiW2lJcHbSy72Nty64rtPPIwChRRc5eKV46RLVFBV5Vc7jF1knHLU12rtsqarXrgk0RAAAAESxunVFKl66xG1dZwlNSXIGabxxtI3WNiKiP2lw7Vh8n5p27ux0e/u/SN26Im368TWeE5odKjtV8c5bhzp19G2+XAcGoubr3p8x3dPnEYDQI6mJbtmbmlS37nBCs6vToLp1RX59kZWueFnO1iEIAAAAiEqBThRUt65I9qYmSS1DzjtLaErBGwru20RBfS9hGO5sDQ1q2rHdj4o2n4pXrHpbDh/rRBOHzRaShKYk2Q4ecH0O9Ka2n0cAegfDz9Gt0pUr3Ja/TB3lcxtP5M3RyNpd+jp5uNKbq5Viq9Wkg0Ua2HhAkmQrL1P9po1KGDM2KDEDAACg93SW3AtGp8fSlSs04LIrVPzAfa511eZ4/TpvVhBa78iXROzDLxfJZApd107DkMxmk+x2h9dD35874vwejSnc7HvuGUldv/ceOnQvxdu3/EHmdunwBpNVTwyb26FOjKNZP9n2yuEVTqcqPnhfGdOmBxxzJKr44H235U/TjtSHmRPd1t24faWeypsdlP21fg4Ew4MB3NczmHEA8B1JTXSruWS/2/K3ycO8qmc4HXIaLZ2BG8yx+jplhCSp3JqicmuK/m6J17U733SVt1VWBiliAAAAhKPhg1I174xRWv7uJq/r1JrjlGhvcC23npva20wKFIyE5ukTc12PZ502Qi+89Z0k3xKxuw/UBhxHuJl3hucODcePzVa/1LgQR+O75gOlkiSro/sedV+kjtFxlRvc1q1JHeO5XVOM6kyxSnA0Hl4XrHtGRqD2z719QlOS/tL/h0HZ1/iqrWou8X0SsGxHtUpMyUGJoVX7a2UAoUVSE92Kye6v+vXfdr7d0awflH2pY6o2u62/bevL+tWIyzqtd9Ca5rZsSU0NKE4AAACElzFD0vTDYwbqm20HdcZxg5WeHKspxw6S1WLSN/9aox989ab+m36USmLTtSVxsMc2HO3SijHZLckMc1KS7I2Nnqp06eqdf9GH/SZqcP1+GXLqu2En6uxTR+nEIw8nSU49ZqCyUuOUk5kowzD05MIfatkb32jPwVplp8UrMS5GcbFmbd5VKWuMSdlp8TrxyAFKio/xOZ5AmM2GkpLiVFPTILvdvYdh3c7v9e57X8lmmGV22rW1k79vq6OrtqjR1BJ/gr1RjjFH67hjh+mUo3MkSUtvPlUvv7dJFrNJRw/L0LGjsmQYhn553YmqaWjWngO1SoyL0YfrduubbWU688QjdM4pQ3vkefsiJjNLzbuLZZZTU0s/0/tZx3datsHccUbrqpjETsvbDfe7ucVkZfsfaITz5rlXWTr/W3ZnaN0epdhqdVTVVg1sPKCY7PEdyiy9+Ydav6Nc2enxKqtu1BH9k/XdznIdO7JlAtsFSTv0/i6bvkkeLrthUlVMksd99Wuq0NC6vdqYdISSbHWyOB0qjvf8/Fo/jwD0DpKa6FbW7Lmq+vCDTrfHOGw6oWJ9h/VmeX+PTEt6huJHjfYrPgAAAISnlESrTjpygE46coBrnWEY+uExA3Xy2BnauuBPOrVsnRoNix4ffqnHNtpP+JM1u2UocO7d9+r7W2/yOaaspkrN2ttybmtJT9dlP/6BDFPHqQbGDs1wPU6Kj9Htl+T7vK+eZrGYlJ6eqPLyWtls7ufeziPSlPrG76TqakndD7E9q+QT12NzerqGXX6d298lIc6ia84e16Fe/4wE9Zc0fGBLB4VjR2X5+3R6xIBrr9e2wh9LkjKbfB8Z5vWEU4ahtClTfW4/WqRNmaoDK1/pvqCf5u55z2259XOgrYS4GBWMaUk+Dunf0iOz7WfPwOuu1ymFP9Yp5V+rxJqm3w051+O+Zu95X6m2Wp1x4HPXus6OH09xAAgdJgpCt8xWqxImdH4S1/ZrPmFCvtKmz/R5H1lzL/V4MgkAAIDo1PYcs8u0UZuNCRPyZba29KaLTUmRER8fUAxZc+dF7TmoYTIp57Ir/aqbHUV/F0tcnKxD8yT5OPO7paX/j7c10qbNkMnSd/sMmSwWv64D/dH2c8AX7u+Fznn7PvE3DgDBEx3fVOhxuYULlZTvObHZ+qGfMCFfuYULlT1rjtdfaKakJOXML1TyxIKgxQoAAIDIkFu4UAkT8rtMIrT2lGs912xr5FPL/EpsGnFxfeIcNHligXLmF0peJiiNmJio/LsMXXSvrEPzfEpWjfrN/5MpJaXLnppOGS09NKfPVPasOcEKN2L5ch3oL0+fA75ofS90xTg065Z1aF6nnXsCjQNAcBjOzqYmhF/sdofKyqLvBuFSy/CWlDiLzl/0ttv6VLNNjyyc3OFXKofNpmse+VeXbf72jslR8yswEMm6Gr4GILJxfKM3vLdml15+b7OOH5utH593VLflG+oatODJTzxuu33AXo26dFaXPaL+58HOb5XU3pPnZCtx7LioOAf19vh2Ohy6+uEPu2zrybOzlDjuyKj4u3Rm/Zb9euT1zucKOC2pQpded7bbe+03f/pSn2066LH8oqOaNXTG1D7dQ9OTzq4DM8zNKrP7d9/ZXwzcqay5lwatZ+TO3WX63z+s87jt1thvNGb+dbLEtUyEZW9q0rWPfeza/tytPwhJD02+v9GXZWQkymzu/vuIT1/4xBwf22GdJTHR44e6N1/u0XzSBAAAAO9YYjtPEGSef2FQEwhJR3afZI023pxzJx11dAgi6V0xcR2vZdpKGj+hw3utq2uatFMnkdD0oLO/iSU5Raqo96vNAVdcFUBEHZljO38vDL72elnavFfavycYcg6EDzJKCJhheHnzbAAAAPQJvg4F6+p0koFlCBbD20l/2nB08f7jrRmduL4FIgdJTQTMxIc+AAAAAtBVEoHEEYKmm8sWT2813n/RqcuJgri8BSIGSU0EjA99AAAABKKr00lySgiW7i9bOr7buuop7OTdGbG6uoalpyYQOUhqImB86AMAACAQXffUJHGE0PD0VuPd57tIP2a5vAUiB0lNBMxk4lMfAAAAQJjz47Kly/xcZOfuekyk/1n8ufcqgN5BUhMB45csAAAA9BRHpGdIEDb8SVZ1PfwcnnT2Nwun4fpd9Q7n+haIHCQ1ETAmCgIAAECPifChrAgf/ly2dPX2453pWSQcskwUBEQHS28HgMjhdDhUtvaLDusDSWpuuuYqyWSSERen+BEjNeC6+bLExQUQJdpy2Gw6uOptVa/+RM6Geln7D1D6jDOVOO5IGSZ+0wAAAOFvx72LVNtUISMpSYP/d7Hi0tJka2jQzl8/JduGb1sKjbiid4NEVChb9Y42vby2ZSE2TnLYVZ1+spSY67H8jnt/rvr+qRp4808Uk5AQwkjDl72pSXtXvCJpcIdttvIKyQiTaz0mCgKiAklNeKV67Rrtfe4Zydbc4aSxuXR/p3W84nDIWVenuq++1LbCH8s6NE9DF90baMh9Xslrr6rinbfc1tWXl6v+uw0yYmI04JrrlTyxoJeiAwAAUS2IPbVam3LW1GjnbTcHr2H0Od3lqpy2Zslub1moq+22PWdToxq3bdX2mxbIkpWtYb98OAhRRq7ipUtUt65IzYZZGj6vw3aHrVmKCY+kZldvBaaMACIHXbXQreq1a7R32dKWhKYHjro6FS9d4rmOH5p2bNeOxff5VRctPCU023I2N2vvsqXeJ54BAAB6iZNJOxAyHd9r3r7/bKUl2nbXHcEOKGK0JjSlaDhmIz1+oO8gqYkuOR0O7Vv+Yrfl6tYVyd7U5Kqz98XnvWq/xJqmAzGpajDFuK1v2rFdtoYG3wOGHDabW0LTIUPb43NUa+74q2jJiuVyOhyhDA8AAMAnOxJyVBaT3GF9lTlBu+KydSAmtReiQiTqbqKgRlOMDsSkam9sP1VYklRrjlNpbFqn5etNsW7LttISNdfVBSPUiGJvanIlNCXpoNXzMVnp4TjuNUwUBEQFhp+jS/WbNspZVeVa/jYpr0OZGKdNklS6coUGXHaF6jdtlGq7H64hSb8bcq7rceH215Rkr3ct73vuGeXeuNDf0Pusig/ed1t+eMTlrsenHVirEyq+dS3by8tVv2mjEsaMDVl8AAAAvvhnZoH+mVmgSQe+0EkV30iS9lkz9MKQs72qH2dvVIM5tvuCiHqmbsYVF6WOVlHqaK/be3HwmVq4bYXiHU2udXuWPKYj7lrkd4yRqHTlCtfj9zILtCZtXC9G452u3gokNYHIQU9NdMlWWem2vCF5aIcy5+77tySpuWS/xzre2pg0xG25+UCpX+30dc2lJZ1u+2fmxA7r/H29AAAAuhPMCTc+yjzW9fjdrOO9rnfx3g/0g4PrJEnHV24IWjyR5vQCz5PdSNL8848KYSS9Jzcr0fV47u5VQWlzW8Igt2VbWVlQ2o0krdeBkvxOaF656++dbhs6IPg9PPuldH5vT7OHCVUT4+gPBoQjjkx0yZLqPnSg7T3fj634TtMOfOZajsnu36HObVtf0iPDL/Nr3zGZWX7V6+tisrJ9Kt/+NQYAAOgNjxzbrAMrX3EtP9jFjOYOH5KluQ2lym0o1Q/Kv5IpJSWgGCPZpaeP0qWnj+rtMHqVYRhaen6Oih95SJJ055YX9VAX7zOv2mw3K5YlIyOg9iJRTHZ/1a//1uO2n25puZWZp+PZ4rDptm0vu5aXnp8TshFkhmHodz+d4nX5RVcW6K5nPlWc1dyDUQHwFT010aX4UaNldHLyZ5L7vRizZs911VFioqcqPhlw7fUBt9EXpU2Z6nVZc3p6y+sFAADQy3w5h/F3IpLce+73qx6iR/yo0TKnpfVY+wMX3tpjbYer1uvAQFjSM7guAeAzkprokmEyacA8z79etj2VTJiQL7PV6qqTc8WPAtqvdWieLHGdDwlA50wWi9Kmz/SqbPbceTI8DK8AAAAINV/OYfxKalqtiuvBZBYig2EyKfuSlpFkwb51oiUrWzEJCUFuNfyZrVYlTMgPqI2suZeG9XUJt9kEwlP4fmogbCRPLFDO/ELJ4j5DueFsGWqRMCFfuYULPddxH43hFevQPA1ddK/f8ULKnjWny4sCIyZGOfMLlTyxIIRRAQCAvsKPU0BJ3Z/DtPJl+LkkyWrVqKef9TMqRJvWaxVTEEaXtb4TLVnZGvbLhwNuL1LlFi70K7FpSc/gugSA37inJrySPLFAaccVKOmFT6Q9DZKkuMGDNfz/rnf10PRUZ8TTz0qP/curfVgHDtKwB26hh2aQZM+ao8wLLpIecf/7D7z5J0ocd2RY/xIKAAD6rs7OYSRpyCNPKC4tTTHPrZYO1nvVXmsdoK3kiQVKyj9WevjDgNqxZPdX3i/u6JM9NNvLLVwoPfiB27rWaw9Pf2fDYlHeQ49wXQLAb3x6wGuGyaSYNpPKJB11dKcJzbZ1vJU2eQoJzSAzWTr+bpF01NGcOAAAgLDm6RxGkis56cvwcxKa6EwwzomzLriQhGYXurr2cBomrksABIRPEPjNm1E/vo4MAgAAALrjcPg7wB0A/McnDxBeSGrCbwYZSwAAAHShp84WHU5SCwAA9HUkNeE3EzlNAAAA9AKHo7cjABC4CPpxgg49QFgiqQmftP1V3ERWEwAAAL2AnpoAAICkJnzibHP/omAPP+fkFAAAAN7gnpoAAICkJnzSNu8Y7I6a5DQBAACiRA+f2PFjOBD5OIwBBIqkJnziNvzci56aPnXm5FsNAAAAXqCnJoBewUcPEFYsvR0AIoetoUE1O76XlChJcthsQW1//2uvatNzG2RKSVHWZVco5ZgJMkwm174OrnpbNZ+ulux2xY8eo6y5l8pstQZt/41VVSp+4D7Za2pkTkpS7t33KjYlJWjt9zSnw6Ha9d+q/D//VuPWrXLYmiWHUxpwgVu5rbfdosTxxyhz9lw1btuqmg3r1bRjuwyrVZb0dMUPH6GY9AzFjxotw2SSvalJpStXqLlkv2Ky+ytr9tyg/N2b6+q0Z8ljspWVyZKRoYELb1VMQkLA7QIAgOi16ZqrJEm2vLmSOXjngYC/9j6zTJtqdkiSTOnpGvzz+4J2DWFvatL+FS+rYeMGGWaLUk46RelnTJPJEh6X8c11dfr+kYfk2Pn94ZUjrnAr43Q4XNd0ABBs4fFpiLC3Y/F9atqxXc2DpknxLUnNsjf+qB0f1mvoonuDsxOnJKdDjsoK7f/1k9pvGMr58Q2q37ZVFe+85Va0ef8+Vf3rQyVMyFdu4cKAd735xvly1te7lu2Njfr+1ptkxMdr5FPLAm6/p1WvXaN9v3tOzsbGbsvaK8pV9a8PVfWvDz1ur/rwn5IkS3q6TKlpatqx3bWtfv23qvrwg4D/7tvuukO20pLDMZWXaftNC2TJytawXz7sd7sAAKBvcDITMcJE2457jvLyoF1DFC9dorp1RW7rDv5xpQ7+caXSps9U9qw5AbUfqPbn853ZfN3/KGd+YQgiAtAX8ZMJutWa0JQkpw6fQBpyqmnHdu1YfF9Q9tOhJ7/Tqb3LlnZIaLZVt65IxUuXBLTf9glNtxDq67X5xvkBtd/Tqteu0d5lS71KaPrCVl7ultBsK5C/e1cnQLbSEm276w6/2gUAAH2HQyQ1EbjqtWt6pN1AryE8JTTbqnjnLZW89qrf7QfK24Rmq73LlvZgNKHBJw4QnuipiS7ZGhpcia0DMakqju9/eOOhLGTTju2yNTTIEhfXob4vt8n8MHOiPsycqJu2vaoER8cE3T5rhl4Ycrbm7H5XefV7Xevr1hXJ3tTk15DoxqoqV0LTLpN+NeIySdJtW16SRY6W51Bfr8aqqrAciu50OLT/5T+4lmvNcXqz/w91ZPU2fdjvWDWbOh7i72cWaOqBlhO4KnOCPsycqCPq96rJsKhfc5XsMmlkXbFbnY2JQ5TeXKXspgrXOn/+7s11da4ToEbDoseHXypJWrhtheIdTZJaEpvNdXUMRQcAIBr4mQlorKryuP7BdkNbAX85HQ6Vrlgu9TsnoHbeHHCq3tSpkqQbtr+mZHvLtYW/1xD2pia3hObGxMH6c85pOm/fRxpbc3iYd8Wqt5V5wUUhH4re9nxekt7KOlFfpo4KaQwA0IqemujSvueecT3+f0ec57Yto7nKY7m2vJlMqL0nh3keSvHCkLMlSa8OOqPDttKVK3zejyQVP3C4l+k/sk9yPf5b/x90Wi6c1G/aKEdlpWv5qbzZ+j4hR//of4rqLPFqNsV0qPN52jhVWFpuIfB03sVan5ynt7JP1vtZx2vlwNP1x4FTdCAm1VW+OC5Lf86ZrN8NObdDW77+3fcsecz1uDWhKUlLhs3ttBwAAOh7/D33irMHd+QKolf9po2ylZcHtc1f581yW/bnfdz+/PrPOadJkv4yYJJ7QadTFR+873P7gWp7nl5niu0yoRlvb+iyrTFD0oIVFoA+iqQmutR8oLTTbcPrdndbzmQydP25RwY9rvaaS/b7Vc9eU+N6/G3KcNfj75KHdlounNjaJDR9UWPpuhdkeUyy63GJNb3Tcr7+3W1lZUEtBwAAopO9pkYX7/E9YXPtzr/oinGHR5H8av7JwQwLUaT1PHp6yWpZD40Y6k5Kc61P+/DnGsKX8+tmH4aAB0vb8/RGDx0o2rpy1z9cj6/e+RfX4//90XE695ShujYE14kAohvDz9GlmMwsNe8u7rB+SN0+t9FEMZlZnbZxwrj+OmFcf7d1xU8tUd2XLcMqXho03X1Yuz9xZvtX35yUJLsX96I0JyX51X5Ps6Smdl/ID21vvt9VX1tf/+6WjAzZy7tPWFoyMnxqFwAAhBcf7kDkkTkpSSMO7tZPt7zY7ZDzn2550W159Kh+mnzu2AAjQLRrPY/Or9qs/KrNh99nTqfUbrTZhMpNmlH6qds6b26F4M81REx2f9Wv/9a7slnZPrcfqLbn891N2JVmO5zUzWqq1NLzc5QwpuXYHNI/ubNqYc0Z8KcbgGCipya6NODa6z2uN9p9mHdWztd2/ZU1e273hTzIvdu7mdu9LRdq8aNGy9RDiU1v+Pp3H7jw1qCWAwAA0cnfcy9zapriR40OcjSIRvGjRsuS3nFEUvvrnM7WecOf97HX59eGobQpU31uP1Btz9OdPtw015zGsQkg+EhqokuWuDhZh+Z1WN/2i906NM/jJEHetuvvSUKrhAn5fk0SJEmxKSky4uO7LGPEx4flJEGSZJhM6n/p5b2yb3/+7jEJCbJ084uyJSubSYIAAOjjvDlH8yT70stkmLjEQfcMk0lZc+d1XO8pqenL7Ketdfy8hjBbrUqYkN9tubRpM0I+SZDk3fm8J9mXcGwCCD4+VdCtoYvu7ZDYbP1Nzjo0T0MX+fdLuqd23XdiKGd+odKmz+y0SMKEfOUWLvRr/61GPrWs05NmIz5eI59aFlD7PS15YoFy5hfKiI0NWptOSZb0jEOvT8eTuED+7sN++XCnJ0KWrGwN++XDfrULAACiS1fnaJ7kzC9U8sSCHowI0ab1PLptj01TFwlMS1a2Rv2/F7zqFBHINURu4cIuE5tp02cqe5bnyVVDofV83tuemtFwbPo+/S2AUOCemvDK0EX3Sg9+4Fq2JCdr2NLf+NxD01O78S+tkYqr3Nb3v+EmpRwzQYbJpOSJBcq84CLpkX+5tqecOllZcy/1u4dmeyOfWub2/CTpiMeeDNsemu0lTyxQUv6x0sMfelU+8Zh8Dbv8QumJTzxuzzzvQuVNPkaGyaRta76X3tsqSUqZPEVZs+cG/Hcf9suHO/y98558mh6aAADAjadztPayrl+gtIkF9AKDX9qfR5tiLJLdvYwlNUV59xw+V+3qfRmsa4jcwoWyNzVJj33sWtfvotlKP2Nar/TQbG/YLx+WadcBaflXnZbpv+BGpUzI59gE0GN6/9MQESl+6NCAE5qtTB6+5FLzj3Uv0+6Le8AVVwVl312JlIRmK19OFjJmntnl62cdNMjVXtu//YDLur8hur9IaAIAEH1C0bsp/bjjQ7AXRLO259GG2SzZ3bOaKQXHe32uGsxriPYdCfrNPDNobQeDObbr68HUYyeGKBIAfRU/mcAvwTxBNbqZNQ+h58dtgwAAAICIZ+LaxGvOvnjR0AefMhDOSGrCLyQiAQAAAEQbk6fLHC59POqLOU0A4YWkJvwSzJwm+dHwxssDAACAvoLOG95z0m0RQC8jqQm/MCwjunF6AgAAAkEPLkQqTz01DX7m96hPHee8BYCwRFITfglmTrNPfRlGiD55fxwAAAD0eZ56atKfwzMuGQD0NpKa8AvDMvoOXmsAAAD0FSaPN9WEJ3SEANDbLL0dACKDvanJfYXTEbS2PX0ZbrrmKslsaenm73RKcfFSzoWu7Vvv/InUbFPs0KEacN18WeLifN6vw2ZT2bvvqPy//5VzT7E07DK37VvvvE2pkyYrY9oMmSzhf6g4bDavy+5c8ric5d9LI67wuH3vC7/T1phKWfplqrQqToodJ0natOB6ydYsOTp5/U0mmTMylXXJpUo+erwMk8kV28FVb6vyPx/LUXZQam7usO9N11zluc2YGMWMGCVTSrIa//vp4fVms2JyBirzwouVdNTRrn0BAAAA/gr09/wd99+rjHPPlwxDZavelr1kvxQbp9jsbFkyMmTNHqC0KVM7XF84HQ5VrStSycoVcpaXSXZ7SzDDL3eV2fSTm6XKipYFs1lxY8dp4I9v8OtaKFBOh0O1W7eGfL8A0FbYZGoOHDige+65R6tXr1ZeXp5+8YtfaMyYMd3W27hxo84991y3dVOnTtXTTz8tSfrDH/6gxYsXu22/6667dNVVV0mSvvrqK913333atm2bTjnlFC1evFhpaWlBeU7RonjpEtWtK3JLQtWu+VzFu/+p3MKFPbdje5skXW2N+6aDByVJdV99qW2FP5Z1aJ6GLrrX66ZLXntVFe+81fXuDx5Q2Z9eV9mfXlfa9JnKnjXH+9hDzPV8OklSdlBf3+VmZ2Oj7Af3yb5vnxwpI6TsQxuaGrtu1+GQ/UCJ9j31hPaZTMq5foHqt23t9m/dpeZmNW/4tuN6u13Nxbu098nHJbNZOdfNV/LEAv/3AwAAgD4v0PtnNu38XvuWLumwvm7fXtfjA6+tUNq0Ga7ri+q1a7T3N7/uOJ67/XJrQlOS7HY1fPO1X9dCgapeu0b7fvecDhgpUu7MkO03HNA3FQgvYdG1yel0qrCwUGVlZXr99dd1+eWXa8GCBaqtre22blFRkU466SR9/vnnrn+PPPKI2/bCwkK37ZdeeqmklkTq1VdfraOPPlpvvvmmsrOztWjRoh57npHIldBsx3A6VLeuSMUevrB9Vb9jR8BtNO3Yrh2L7/OqrDcJzfYq3nlLJa+96k9oPc6f5+P05WTN329uh0N7ly0NLKHpLbtde5ctVfXaNT2/LwAA4AOG8iKyeBr8E/S7MTmdruuL6rVrtHfZ0oBuUOnLtVCgWuN1Njb6dk0BAD0gLJKaX3zxhYqKirR48WINHz5cF1xwgfLy8vTee+91W7eoqEgnnHCCUlJSXP8SEhLctp988slu261WqyTptddeU3x8vO6++24NHjxYd955pz777DPt27evx55rJLE3NXlMaEqHT0/r1hV1HJruA1tDgxyNDX7Xb6tpx3bZGrpuy2Gz+Z1kq1j1tk9DvEPB3+fjjNLzj5JXlsvZ2dB4AAAAoBseJwrqoeRdxTtvae+LLwSlLW+uhQLldDi0/+U/HF4mqQmgl4XF8PP169dr4MCBGjFihGtdfn6+vvzyS5133nld1v3iiy/0zTff6Nlnn1VsbKxmzpypO++8U3Fxcdq3b5/27Nmj+++/X9u2bVN2drauvPJKXXHFFa79nnTSSYqJiZEkxcbGasyYMVq3bp1mzJjh9/OxWMIiVxyw0pe76pl4+JfEstdfVc4VV/q1jz2/fVZSZof1D3YzjLp1+1W7/qYBjWWu9SW/fVZDFt7cab2D73/geuyU9FAX+3lwxBXKbtO2JFl+/ZEsKcldxhZKtqpq2QafLUmqtiR0U/qwVwZN73L7mwNO1Sr7CTqyepvWpo11re/udZGk0w6s0QkV693W7Y7N1B8Gn9llva7avmjPB/rjwCkd1ufW79dlu99xLdsrytW0dbMSx47tUBZdM5tNbv8DiB4c3+gNZnNLssNk6vlz42g59/YHx3fwmTwlNU2G1++zb5PydGTNdm1LGKiVA0+XJOXWl6g4PltD6/Zo7p52HXfa3Gbrv2nj9M/Mzm+n1P58+adbXnRb7u5aKFC1GzbKUVnpWt4Z37/L8tF0bFos5jaPQ/O8OL6B7oVFUrO6ulpDhgxxW5eamqoNGzZ0Wa+iokI1NTUqLCzU6aefrl27dum2225TfHy87rjjDn333XcaMmSIbr31Vo0dO1affPKJFi1apCOOOEKTJk1SdXV1h/t2pqamav/+/X4/F5PJUHp6ot/1w8nustJOt42v2uJ67Cgr9fs5by8r1Qnl+7UrfoBf9V8ZOE23bF/hWrZ3E0tZ5eEkZZWl+5hLYjPcV9RLqq/xWLZ3GFL7GIOkwRzrltD01j8zCzokNbtLaHbHU0JTkoo9nEjF2uqj5hjsDSkp8b0dAoAewvGNUIqLaxkZZbVaAv5evuHiY/Tr17/sdDvf+xzfwVAwtr/WbNivWaeP0lMr17ltm3lKXof32RGOCn1vSuvQzl8H/FBHbtnuSmhKUnF8yw3qdyQM7DKGrhKanthlyNyms0l310KBstnc78v/cb8JXZaPpmOz+VCvVEOhf14c30DnwiKpabFYFBsb67YuLi5OdXV1XdZLS0vT6tWrXcv9+/dXYWGhnnjiCd1xxx2aPHmyJk+e7Np+/vnn65NPPtFf/vIXTZo0SWaz2a/9dsXhcKqqyv/64cSUkdXptiENJW7lysu7v/+pJ+aMLI34/gvN3b1KH2dMcH3he6vRbO3QXlexOFMPJwAdXgyXOHffvxRvPzw5TvppU5RSED6T0VStWaPyf7b0Pn110Bkdtp+5/z/aFd9fJ5d9pXhHo54YdkmoQwy5Rku83+/HvsxsNiklJV5VVfWy2xnCD0QTjm/0hvr6ltsTNTXZAv5ePmFMlsrPGKnXPtiqAf0S1Nhk1+D+SeqfkaDZp43o09/7HN/Bs+D8I7V/0jAN7JegRwtP0brNB7SvrE5H5mUoLd7S4X12TUqx/rV9o9aljFRpD3Uy6J6htiPoursWClSjpfPk2vDaXYqzN6nWEq+BDQd00bRxUXVsVla2DO13SiF7Xhzf6MtSUuK96qUcFknN9PR0HThwwG1dTU2N696XvsjOzlZpaakaGxs7JCxbt3/22Wed7re6utqv/bZls0XHB07GxXNU/sH7HdYn2eo6lPP3OWdffZ1qCn+sofX7FHfgM71waCi1v7Kvvq7LWJInTdH+V16WJDm9uOP3kPp9SrIfujeNYWjEuafJZAmLw0aS5Bh4mrb8Y3mn28dXb9X46q0hjKh3mdPSZR0+MmqOwd5gtzv4+wFRiuMboeRwOA/9H5xz49MnDtbpEwd73E/rvvoyju/g6J8WL7vdqfSkWJ2WP8i13tPftv8112li4Y81sXKj/tL/h9qQnOffThOT3Iag+6L9O7+7a6FAWYePlCk11W0IeqtZe/95eMEwlDq5MKrek/ZDz8XpDP31Psc30LmwuDnDhAkTtHnzZlVVVbnWff3118rJyemy3kcffaQbbrjBbd3atWvVr18/xcbG6qmnntKzzz7bYXtru/n5+Vq7dq1rm9Pp1LffftvtfvsKs9WqhAn5XZZJmJAvcwBJYEtcnKxDD50ABHg+ah2aJ0tcXJdlTBaL0qbP9LrNtmnPtGkzwiqhKfn+fKJd9iXzZHiashIAAAAIorbXMf5Ol5M2faZyrrgqKPF4cy0UKMNkUv9LL++2XDheNwGITmFx9T9y5EgNGzZMjz32mBwOh7799lutWrVKU6ZMkcPhUFVVlex2e4d6Rx55pFavXq1nn31W3333nZYvX67nn39el1/e8kE7fvx4Pfvss3rnnXf07bffavHixVq3bp0uu+wySdL06dO1YcMG/f3vf5ckvfzyy6qqqtIpp5wSuicf5nILF3aa2EyYkK/cwoUB72PoontlHZonI4CspnVonoYuuterstmz5iht+kyvZusznC0xpU2fqexZc/yOrye1Pp/OGHFxyplf2G2COuhMJuXMLwxN0tVsVs78QiVPDJ9bAwAAACC6ua5jnD72ojMM1/VF8sQC5cwvlLwYRdaxnZb/fLkWClRrvIaHUZFtnxcAhILhdDrDYrzG+vXrdf3116u5uVnV1dU677zz9Itf/ELFxcWaOnWq3njjDY31MKPxZ599psWLF2vbtm0aNGiQrr76as2aNUvGoS+F5cuX65lnnlFFRYXGjRunn/zkJzruuONc9d944w3dc889SklJUUVFhf73f/9XF198sd/Pw253qKwseu4d0sre1KRrH/tYkpRituvRhZMC6qHpyfZdB/V/yzu/CXxnnr35ZL9+lSzeV6l7XljbZZn/HVen3DMj45fG/3nwgw7rnjwnW4ljx7l6L3oq0xOemJmh5KPHh2S/S87sp6SjjqaHZoAsFpPS0xNVXl7L8BYgynB8oze8/d+dWvnPLTr5qAG65uxxvR1O1OL4Dg/P/eVrrd5weJLVJ2ak6+a3yz2WfeTYZqVNmdrh+sLpcOjqhz/0ab93x67TEfMX9HgPTU/ax/vIsU1Km3J6RFw3+eNgZYNuX/aJLGaTnr19ckj2yfGNviwjIzFy7qkpSePGjdM777yjzz//XOnp6Ro/frwkKTc3Vxs3buy03vHHH68333yz0+3z5s3TvHnzOt1+/vnn6+STT9ZXX32l0aNHa/DgjvfqgdwSmOaEhKAnNCXJ4unXPm/q+fklbpjN3ZbpN216RH8xJx15VK/sN+WYCSHbV/L4Y0K2LwAAAKA9k8X9uiJlQr70tucf9TOmTfe43p8f6AcXLpQlpvtrmp7QPt6MaTN6JY5Q8acjLYCeF1bZmoSEBE2aNCnk+83Oztbpp58e8v2id3nTR9nw+w45AAAAAPoCUy9lvMJiyCUA9CLGa8JnPfWVHepTAYcXWU1+kQMAAPCdk3QL+hCTqZcuGjjMegF/dCCchFVPTfRte+v2h3R/XvXUJKnpt53Vxfp07xptqdguiSHiAAAAiE5Gr/XUJMEGoG8jqYleV1J3QMs3vKZNe0sl/aC3w3HTWycoke7xtcu0pXK7TIZJDqdDJDUBAAAQrXrrkiE8pvwFgN5DUhO+C+KX9uf7ivTShpWHhoInBK9hL3gz/Ly3RpJEum2V30vSoYQmAAAAEL1MvXQffpKaAPo6kproNZ/vK9IL6185vMII7beydycBZDX94RDJTAAAAPQRvXbJQFYTQN/GREHoFSV1pXppw8pejcGbe9Aw+hwAAMB/nEqhL/Dlfe4MYvdKUpoA+jp6asJnRhBOT5dveN2r4d/e2HTNVS0PTGbJZMiUkKDUqWeo3/SZMlkOv8VtDQ3a/Zun1bhxg9TcrN1xmVLumV22bSKrCQAAAKAr7S4ZNl1zlTTiCo9FN177Py2zpSenyGS3SYmJMsXFy1FZKWWe49NuGX4OoK8LOKlpt9tlNpuDEQsiRKB5vp1VxdpSuT04wbTlsEsOyVFVpfI//1Hlf/6j0qbPVPasOdqx+D417XDfp9OL5Cw5TQAAAABdqfrPx1LsUK/KOiXJ4ZAqK1pu2FRTc/jGTZk9ER2CiUQyEF4CGn7e2Nio888/X59++mmw4kEf8Om+NTIZobnzQcU7b2nLrTd1SGhK3iY1yWoCAAD4jAt/9BHb7rpDzvo6r8t7cw3idVtk2AD0cQH11HzmmWe0fft2paenByse9AFbKrZ7nBXbZPdvcplSa6pshkUmp0MxTpvSm6vdThUcVVWux5WWRJXFpCjRXq89cfwU2lMcNSmSySE5TJKtZ+9y4XQ4ZJi4PTAAAABCq7muTrbSEilziNv6fbEZndbZljhIeXV7VGpNU0ZztQynQ1anTXtjfb82KamoV12Dzed6ABAt/M42fPLJJ3rmmWd0zz33aPfu3SouLpbF0rE5m82mxsZGnXlm1/cuROSIswZ2u4F9tSUe14/ZWa8iP9r77ZDzOqz76ZYXO6xblzJSb2ef5Mce4KvG9SeHbF/1mzYqYczYkO0PAAAAkKQ9Sx6TJFkc7onFFwaf3WmdP+WcFrT9P/Di2qC1BQCRyK+k5ieffKIbbrhBt9xyi+bMmaNJkyapqqqq06Rmc3MzSc0ocONFR+uNj3do/nlH+t2Gw+mQ3Wn3uC27prrLuinNtRpat0dfpY70a9/eJDQzmipVZk31q/3edtXMMXrhre9cywsvHt+hzN1XTPR48tNPDTIaG3QgNq0nQ+wRtsrK3g4BAAAAfZCtrEySdELFeq3O6Hju3dPiY3t33t/6xpZk7lknHdGrcYQCtyUDwpNPn4KbN2/W22+/rT/84Q+6//77dc45LbOzffTRRz0SHMLLcWP7a9rJw1ReXiubzb+h4ibDJLNh9pjYrEox6adfvKgXc2dqT1yWa/2dW150G05+ZulqVViS9JuhF/oVgyfte3bm3nZnxPX+O/WYgTr1mIFdlhk+MFW/++mUDuv3vfSiqj78QJLUYLLqiWFz3ba3//tkzr5EGdOmq+67Dbpz1+/kMLe8Qg1f/lDOxkSv4m3f5utT07S7v1WnfV6t8ZvrJUn/zjhG/8k4pkO97fE5enXQGcpqLJMlNcer/QEAAADBZMnIkL28THGOJl1e/A/9ITc4HXnanienTJ6iAZd5nkkdAPo6r29Et337dp133nl65pln9PTTT7sSmoCvBiRme1z/1Yh4Obr4AczZ5l9P3n3enJau+FGje6z9cJQ1u20Ss5u/rWEobcpUSVL8qNHKqAnstXBKqk4waU9WjCRp8ykjvK5rmC197rUCAABAeBi48FbXY6OHLk/cz9MBAG15ndTMysrS/fffr6FDh+rHP/6xXn31Vde24uJiVTIEFF4akZbncfZzh8WkL8bEe6zTeo5QG+d7t39TSopP5bMvmdfnJp4xW61KmJDvVdm0aTNkOnSrCcNk0sisUTIc/p3Ftdb6aGKSnCZDJsOk4dnDXbEYnczo2PouMKel9bnXCgCAiMKITUSxmIQEWbJaO2wEP6uZMCFfZqs16O0CQLTwOhuQlJSkiy++WH/961+1aNEiPf7447r99ttls9k0f/58nXjiiTr66KM1adIkzZo1S/fcc4+KivyZ9gXR7sScAo+zn0vSf/KTVRvf8W3pNKS1Y+P12wuztC/DIsPLk4a06TM14rEnZR2a131hk0k58wuVPLHAq7ajTW7hQiVMyO/yV+a06TOVPWuO27pTjzlTTpN/Vyw1CSb9/Ycp2jo4TlLLPVdPzClwxdKpQ/szx3tOggMAAAChMOyXD8uSlR30/H3ChHzlFi4McqsAEF18vrOwYRg6//zzddxxx+maa67RDTfcoKefflqS1NTUpNraWu3atUsffPCBLrvsMp111lm6//77FRcXF/TgEZmGJOdqRGqetlV+L4c6JjerEsxS7eHlj/IT9fXIeDksLcnOlTMyZKq2SBs630f6BRep3/SZrh6FQxfdKz34QaflB9x4s5KPHt/ne/3lFi5Uek2dtPRTt/Xt/55ttX09ffH61DTtyYpxJURNMmlY6hEakpx7OJaPtkird7rVG3DjzWpOzpVWfuXT/gAAQOj03I2CgPAz7JcPy769RHr1m6C0N/zpZ+mhCQBe8DuDM2jQID3//PP67rvv9PTTT2vw4MEaPny4xo8fr7POOkuPPvqofv/73+vjjz/WggULghkzosC8sbNk8nIGuXVjEl0JzVb2WHOXdbLOOsdjAq4zKcdM6PMJzVaeTqC6+3v68nq22t3f6tbD02QYmjd2lnss5o6vc8oxEyReKwAAAIQRSxA78ZDQBADveJ0ZKCsr04033qh169a51g0YMECPPvqoJkyY4LFOQUGBfvOb3+jKK68MNE5EmeyETF0+drZXZX3MlSFAhh+DZ3x5PTtz+djZyk7IDKgNAAAAoDdwzQIAoed1V7Zt27Zp8+bNuuSSSzRu3DhNnjxZRptP7qVLl3qs53A41NjYqEmTJgUeLaJKwYB8OSW9tGGlHE6nx6Ho7ZlkkskwdNGo8/XCOian6hF+npAVDMhXsrVSVY3dv46tWl/Py8fOVsEAD/fQ7CSW1tUMbQMAAAAAoG/yOqlZUFCgt99+W5s2bdJLL72k3/3ud2poaFBOTo6OPvpoOT3MUuxwONTU1CS73R7UoBE9jhuQryNSBmv5hte0pXK7x1nRJclkmORwOjQs7QjNGzNLcc4USR+HNtg+IpAfmePMsapSvdflW19PemgCAAAgkvkz2gmRx0PaA0Av8nmioFGjRun+++/XjTfeqMcff1xvvPGGjjrqKN1zzz3KzCQxAd9lJ2TqlonztbO6WJ/uXaMPN5jV0GZ7btJADU8bqhNzClyTyFTVNfVOsH1AqIbO3HncTa7XEwAAAAAAwBd+z7aRlZWlX/ziF/rtb3+rL774QuvXrw9mXOiDhiTnavao8zUoKcdt/V3H36zZo853S4DxO2hPCs1f15uEJq8zAACRjd5r6Cu4pyYAhJ7PPTXbO+mkk/Tee+8pLoizvQHdMThr6DHh9KdldAcAAAAAAPDE756adrtd77//vmpqajpNaO7atUtXX32138EBgCeuvCtZTwAAwpKn++0DQKQKp44fAA7zu6em3W5XYWGh/va3vykpKcljmaamJn322Wd+Bwf4a9M1V3VcOeKKkMcRicLpCzuMQgGAiGBvalLpyhVqLtmvmOz+ypo9VxYLo2kAoKcxkgwAQs/vpKbVapXT6VRMTIzb+lmzZik2NlYmk0mNjY0dtgMIb9z7CgAiU/HSJapbV+Rarl//rao+/EBJ+flK/99FvRgZAMAXJa+9quxZc3o7DHjgZKgYEFZ8Smru379fl1xyiaxWq0wmkwzD0M033yyr1apBgwbp7rvv1tdff63LLrtMMTExqq6u1o4dO3oodAA9gpwmAESc9gnNtmqKirThgQc1YMGNIY4KAPqOYJ5CV7zzliSR2ASAbviU1LTZbNqzZ4+WLFkip9Opm2++WTNmzFBKSoqWLl2q119/3ZXoTEpK0s6dO/X+++/3VOxApx5kqLnfyGkCQGSxNzW5JTRXpx2l8phkzSj9VKZDPUrKPvtcWdc0SaaA54gEAHgS5JPoilVvK/OCi2Sy8LkNAJ3x+RPSMAxNnz7dtTxz5kwNHjxYn376qfr16ydJ+vLLLxUfH6/9+/cHL1L0GXk5Kdq6p6rLMjFmv+e4QjcCuR/QiNxUlVTUBy2WAf0SPW/gnkUA4FK6csXhx9Y0fZR5rCQpt6FE46u3uraVrHhFWZdeHvL4AKAvSIgNcvLR6VTFB+8rY9r07ssCQB/l1yev0+nUxo0bXcmPmpoaPfzww7Jarbr77rv185//XFLLZEKAry6cNExl1Y36YlOpZp82wmOZWKtZg7IStbu0NuD9/Y9lo6QpAbcTTY7on6zv91dLkn583pFe17to0nAVbS5VfaPnY//xwlP0ry/3aPzwTK/aKxidpdgYsxqbW9q767Jj3bZzRxsAkJpLDv+IXGOOdz3emjjILanZxI/NANBjMlLilJZkVUVNk9v6M1Mr9Y/KVI2u+V4bk47otp0bt690PW4uLQl6nAAQTXxOajqdTr377rt69dVXXet+/etfa8uWLXruueckSW+++aaSkpL0/fffa+7cucGLFn1CnNWiwguP7rbc/119goqfWqK6L1uG3P037Uj9M3Nit/Wu3PV35TQedC2nTCah2d69PzrOr3rpybH69S2Tuixzzil5XrdnGIaW/aTr9gCgr4vJ7q/69d962OLeq93av39oAgLaYnAF+pDHCn/QYV3Zqnc0fuWLruWubpP10y0vui3HZGUHLzgEhI8yIDx5PYZ3yZIleuaZZyRJH374oS677DJJ0sGDB7VixQpdc801HeoEMowV8MaAa69vs+Rfv72s2STeAQCRq7PvsfbfitlzL+n5YAAAbtKmTPWvomH4XxcA+give2o2NTXp7bfflmEYuueeexQbGyun06mysjJdfPHFOuGEE1xlr7/+epnNZjU0NPRI0EArS1ycrEPz1LRju9d1jDaXeQkT8mW2WnsiNAAAQsJstSphQn6ns59LUsbxx8lstcpmc4QwMgCAyWJR2vSZrhnNvZU2bQaTBIUj7n8FhBWve2refvvt+tOf/iRJiouLc/XCHDlypO6++263suedd54uvvhizZgxI4ihAp4NXXSvrEPzZDi9+4ZpLZcwIV+5hQt7MjQAAEIit3ChEibku61zHhosl5Sfr7F3/7Q3wgIASMqeNUdp02d6XT5t+kxlz5rTgxEBQHTw6acfwzDkdDo1efJk1/IvfvELTZ06VWeffbbi4uIkSWeeeaaSkpK0c+dO1302gZ40dNG9+u6TbdK/dnRbNmligYZfPosemhGs9cYWTi8T2QDQF+QWLlT55v3SH1vur2nJzNLwe55VbEJcL0cGAMieNUeZF1wkPfKvTsv0u2i20s+YRg9NAPCST5+WKSkpuvHGG13Jy1/96leKiYnRo48+ql//+td69NFHJUm/+93vZLVaVVlZGfyIgU6YY2K8Kpd1znkkNAEAUclkOfxdGJs7mO87AAgj3SUr+808M0SRAEB08CmpmZycrBtuuEGSZLPZ9Mgjj+jWW29VZmambr75Zv373//WkCFD9O6778psNqu5uVkOB/duQoh4OS8V81cBAAAAALzGRSQQlvzu197c3Cyn0ymbzaakpCQ9+eSTslgsWrjw8D0Kt27dqtmzZwclUKA73n7NGHwhAQCilLPNDAbcogMAAADRzO+kZmxsrJ5//nkNHDhQkpSQkNChjGEYGjx4sP/RAT7wNllJThMAELWcHh8CvYbTLgDRhO9WILx4Pft5q/r6ek2dOlX79u3TSSed5DGZ2Wrt2rW65pprAgoQCDZ6akY+XkIA8IyLLQAAAPQVPvfUNJvN2r17t8xmc7dlX3/9dVmtVp199tl+BQf4wttEFwkxAEC0ajvknNHn6E28/wAAQE/zOalpPTSL5j333KMhQ4YoNzdXEyZM0JFHHilLm9nc3nvvPX355Zd66aWXghct0AXvh5+T1QQARCeH2/BzskoAAACIXn7fU3PgwIGqqKjQunXr9MQTT8hsNuuMM87QvHnzZLFYdPfdd+tHP/qRCgoKghkv0ClvU5WO5mZJ8T0ZCgAAIWdvatLBD96XlCVJqv32W2266TkZMRY5bc2SYZJsNhkms+JHjNCA6+bLEhfXu0EDAFycDocMk893iEOIHXz7LTVs2SxTXKySTzpFiWPH8boBvcTvpGZhYaH69esnSbLZbPr888/1yiuvaNasWbJarTrrrLN05513Bi1QIFh2/fwu2Y4apdzChb0dCgAAQVG8dInq1hWpPnGwlHNay0qnU6qr7dBf0ymp7qsvta3wx7IOzdPQRfeGOlwAgAfb7/yJsubOU/JEOgaFm7YdaA6+/qrrcfWnq2XExmnA/1zD6wb0Aq9+TqiqqtLFF1+sv/71r2pubu6wfcuWLfrvf/+rL774QnFxcYqJidH+/ftls9mCHjDQGV9GldetK1Lx0iU9FwwAACHSmtCU3CcK8mbwedOO7dqx+L4eiQsA4Btbebn2Lluq6rVrejsUtHPgzTc63eZsbOB1A3qJV0nNHTt2qLKyUrfffrtOO+00GYahyspK/fa3v9UJJ5ygCy+8UJ999pkKCwv173//Wy+99JK++eYb/d///V9Pxw+4+HqvzLp1RbI3NfVQNAgFJiEA0NfZm5pcCU1Jcnp9M5bDmnZsl62hIZhhAQACULriZTkdjt4OA4c4bDZVfvhBt+VKVyzndQNCzKvh5+PHj9e7776rbdu26U9/+pP+8pe/6KKLLtLgwYNVWFioM8880zUUXZLGjBmjxx57TNdcc41+8IMf6IwzzuixJwAEonTlCg247IreDgMAAL+UrlzhtvxGzmTX4x0JA/XIsEtlM3U83bt+x5+UbqtxLe977hnl3shtWQAgHNjKy1S/aaMSxozt7VAgqeKD992Wv03K018H/FAnln+jyQe/cK23lZfzugEh5tPdbIcNG6bbbrtNH3zwgX72s5+poaFBS5cu1TfffNOh7CmnnKKLL75Y9913nxr49R8hMG5oulflEuyH34/NJft7KhwAAHpcd99jnhKakvTM0Avd2zlQGrSYgLZ8HEgD4BBbZWVvh4BDmktL3Jb/OuCHkqRP04/qUJbXDQgtv6boiomJ0axZs/SPf/xD1113nfLy8jyWKyws1EUXXaQ4ZtZECGSmxuun847VoKxEjYqrV17tbg2qL9Hg+n1u5WKc9sOPs/uHOkwAAIImWN9jMZlZQWkHaMUdYgDPHj27v049+IUKt7+mKaWfu9YXVKx3K2dJTQ11aOhETFa212V53YDQ8mr4+e7du3XbbbcpJibG4/aPPvrI43qHw6HGxkbdcsst/kcI+GDU4DT939UnyN7UpK0LrnOtf3CE5yHmWbPnhio0BJGv908FgGiVNXuuqry4z1d3Blx7fRCiAQB0J23cWJ2q/yebvV7HV27QB1nHSZIsbTpeWNIzFD9qdG+FiHbSpkzV93/8c7flLOnpvG5AiHmV1ExMTNSUKVMUFxcnk8lz506n06mnnnpKV111lVJSUiRJdrtdjY2NwYsW8JLZalXChHy3yRPaS5iQL7PVGsKoEGz0AgHQ13nzfdcd69A8WRhVAwAhYZhMypo7T3uXLXVf3+bENmvupTI6ue5G6JksFqWeNkXa0XW5rLnzeN2AEPMqqZmWlqZrr72223Lvvvuuhg8frmnTpgUcGBCo3MKFKl66xOOFXsKEfOUWMiECACDydfV91x3r0DwNXXRvD0QFAOhM8sQCaX6hSlcsd1tvSc9Q1txLW7YjrGSee7705McetxlxcRrwo2t43YBe4FVSs62PPvpI+/fvl9VDDzen06kPP/yQpCbCRm7hQtmbmqTHDn8BDX/6WXpoAgCiiqfvu+4MW/obemgCQC9JnligpPxjpYc/bFk+8UTlnX8jPf0izMBbblPi2HG8bkAv8Sup+c9//tPj/TX37dunLVu2yG63y2w2ByVAIFDtE5gkNAEA0cjX7zcSmgDQu9omwqyZmSTGIlDSkR1nQAcQOj4nNe+55x7dc889HreVlZWpvLychCYAAAAAAACAHuNzUrMrGRkZysjICGaTAAAAACKW0dsBABGCYwUAfEX/dgCRy8n85wAAhCW+owGfkNIMb7w+QHgiqQkAAAAAAAAgopDUBAAAAACgFxl0BQQAn5HUBAAAAAAAABBRSGoCiDj8kg0AAIBoYnCCCwA+C+rs5wAQSkxBAACHOR0On8pvuv5qmVNTFTt4iPpffZ2ad34vW2WlLKmpih81WoaJ374BIFRIaUamTddc1fLAZJIpKUmpU89Qv+kzZbKQagFCgSMNAAAgwlWvXaPSFculfud4X8lul72sTHVlZdp+0wK3TZb0dGXNnafkiQVBjhQA4BFZzcjmcMhRVaXyP/9R5X/+o9Kmz1T2rDm9HRUQ9fgJHgAAIIJVr12jvcuWylZeHrQ2beXl2rtsqarXrglam+ibGFELIBrs/e2zPpWveOctlbz2ag9FA6AVSU0AAIAI5XQ4Wnpo+qE8Jll1pljXcqMR06FM6YqXfR7WDkjcIgbwFfn/8GVvalL911973HYgJlX7YjNUYk2To92rWLHqbTlstlCECPRZDD8HEHEMTvsAQJJUv2mj3z00nzniAknSwm2valPSYL2VfbKmln6m4yq/c5WxlZepftNGJYwZG5R4AQCeWSz0NwpXpStXdLrt/x1xntvyT7e8eHjB6VTFB+8rY9r0ngoN6PP45ESfkBjXkr9/4NoTejkSAACCx1ZZGXAb3yUdobeyT5YkvZ91fI/sAwDg2QU/zNPQAcmaPGFQb4eCTjSX7Pe/bmlJECMB0B49NdEnPHXzqb0dAnoCY9sA9HGW1NSo2AcA9FXnnJKnc07J6+0w0IWY7P7Sd1v8q5uVHeRoALRFT00AAIAIFT9qtCzp6QG10dXvQ5b0DMWPGh1Q+wAARLKs2XP9q2gYSpsyNbjBAHBDUhMAACBCGSaTsubO67H2s+ZeKsPE6SIAoO8yW62KP/pon+ulTZshk4XBsUBP4iwVQORhniAAcEmeWKCc+YV+99j09JFqSc9QzvxCJU8sCCw4AACiwMBrrvepfNr0mcqeNaeHogHQip8NAAAAIlzyxAIl5R8rPfyhz3VjcnOlpsPLubfdqfhRo+mhCQCAj9IvuEj9ps+khyYQIpytAohYTmYKAgAXf5OQ6ZOnuC0njBlLQhOBO/QVzeAKAH1J1lnnkNAEQogzVgAAAAAAAAARhaQmAAAAAAAAgIhCUhMAAAAAAABARCGpCSDicH8uAAAAAKFicAEChCWSmgAilpN5ggAAAAAA6JNIagIAAAAAAACIKCQ1AQAAAAAAAEQUkpoAAAB92P6XXnRb3v6/P9e+l34ve1NTL0WEqMKN6AD0IZuuu1rbFt2lppqa3g4F6BPCJql54MABLViwQPn5+brwwgv13XffeVVv48aNGj16tNu/BQsWeCz7l7/8RVOmTHEtO51OnXDCCW51CwoKgvJ8APQgro8AoMc0F+9S1Yf/1NYF16l46ZLeDgcRitteA4gmpW/82atyToddtn17tePmQm259aYejgqApbcDkFqSi4WFhZKk119/XV999ZUWLFigv/71r0pMTOyyblFRkU466SQ9+eSTrnUWS8entW/fPi1evFjJycmuddu3b1djY6M+/fRTmc1mSZLBr8kAAKBP6fzcp25dkYqXLlFu4cIQxgMAQPgoee1VVX3wvjRsrk/1HFVV2nLrTRrx2JPdFwbgl7DoqfnFF1+oqKhIixcv1vDhw3XBBRcoLy9P7733Xrd1i4qKdMIJJyglJcX1LyEhwa2M0+nUXXfdpQEDBnSom5+fr/T0dFfdtklPAPj/7d15eFTl/f//1yyZ7MkkkIRAgAABARUTiFDrWlAW61YVoS5oXSpolKr9KVY+tYtVay1FpFqXamsVUfQrat1opWqt1AKCKPuOrAkkIfsyM+f3R2SYyTKZTGYyM8nzcV1czLnPfe5zn5zlPud97nMOAEQLl8MR0HTLMsd5Da9PGuQ1XLN2DY+iAwB6JJfDofJl78vf/ue/zZshh+l4mMVVUcGj6EAIRURPzQ0bNqhv377Ky8tzpxUUFOjLL7/UxRdf7HPaL774Ql9//bWefvppxcbGasqUKbrnnnsUFxfnzvPiiy9q//79uvfee/WrX/3Ka9rdu3frtNNOU21trb7zne/ovvvuU//+/Tu1PFZrRMSKg85iMXv9D4SL1WMb7K77W1dj/wai35EPlysYp3Zv9zlTJ27b6ZVW+toryp5xbafLRs9hNjf1ADabaKtDifYbCK0jHy6XDKNDr796asAPdOvu193D+x55SEMffKjD82b/BtoXEUHNyspKDRgwwCstNTVVGzdu9DldeXm5qqqqVFRUpHPPPVfffPONfvrTnyo+Pl533323pKZHzOfPn6/nn39etbW1XtPv3LlTZ511lq6//no1Njbq17/+te644w699tprAS+L2WxSWprvR+ajXUpKfLirgB4uuaJekmS2mLv9/tbV2L+B6FV6tFRSps88M755Vy/0P7/DZbtKSzjeokPi42IkSbGxMWw7XYD2GwiNpra1YypjvI95rsryTh0H2b+BtkVEUNNqtSo2NtYrLS4uTjU1NT6ns9vtWrFihXs4KytLRUVFmj9/vu6++245nU7dc889uv766zVq1Ch9/vnnXtMvWrTIa/jXv/61zj33XO3YsUODBw8OaFlcLkMVFb7rHa0sFrNSUuJVUVErp9MV7uqgB6usrJMkuZwulZVVh7k23QP7NxD9jNR0JThqVWP1vvgZUv2Nph74l3t41q7X9WTuZR0q25yewfEWHVJb1yhJqq9vZNsJIdpvILSM1PRW0+dse0G74vtocb+J7ZZhTrYHdBxk/0ZPlpIS71cv5YgIaqalpenw4cNeaVVVVbLZbB0uKzMzUyUlJaqvr9ezzz4rs9msmTNn+j2tJO3duzfgoKYkORzd+4DjdLq6/TIisjmdx99pw7YYXOzfQPRKPnu8tOKDFunNn5gzBfBd6vTLp3FsQIe4XE3bmcugre4KtN9AaCSfPV6HFr/cqTL63X1vp/ZP9m+gbRHxcob8/Hxt3bpVFRUV7rSvvvpK2dnZPqf7+OOPdeutt3qlrV69Wr169VJsbKxef/11bdy4UePGjVNhYaFmzpyp/fv3q7CwUCtXrtSUKVO8eoOuXr1aktS3b98gLh2AUDE6fl0OAN2W2WqVyRbbIr3FobKDx86E/AJZArjRDABAtDNbrbJPnBz49CkpsiUlBbFGADxFRE/NoUOHavDgwZo3b55+/vOfa+PGjVq2bJmeeOIJuVwuVVVVKTExURaLxWu6E088UStWrNDTTz+ts846S6tXr9bzzz+vWbNmSWp6vNzh8SXQL7/8Ur/97W+1aNEiZWRkKCkpSffdd59uuOEGlZSU6Ne//rW++93ven2wCAAAIFqY42KlmsZmqd9+sCUuTtY+2TJ9c9Dv8hLyC5RTNDuINURPYXx757ED39YAgIiUOXWa6pwmaVfHpjOnpChv3oKQ1AlAk4gIakrSQw89pJtvvlnvv/++KisrdfHFF+uss87S3r17NWHCBC1dulQjRozwmqZ3797605/+pAceeEALFixQv379NHfuXE2dOlWS1KdPH6/8+/btk9VqVU5OjiRpwYIFmjt3rq688kqlpKTowgsvVFFRUdcsMAAAQJC11oPdmpamkTf/n4wBQ+R0ScmlldLTK32Wk3LO95RxxQ/poQkAgKTel1wqzf/k+PAVP1TdwFHSG61/3Dh3/kJ6aAJdIGKCmiNHjtQHH3yglStXKi0tTaNGjZIk5eTkaPPmzW1ON3bsWL311lt+zWPcuHFavny5ezg7O1t//vOfO1dxAF3ORLcPAPBbbP8BSivIb/pIgcslc0z7gco+V1/bBTUDACA6pU+cpIO72v4yOgFNoGtETFBTkhISEnT22WeHuxoAAADdFjeGAADoPJpTIPwi4kNBAAAACI3mj6RzEQYAQOfxzVIg/AhqAohinEoAQHuM5sdKumoCAACgGyCoCQAA0J01v//T2teEAAAAgChDUBMAAKAbax7CdBHTBACgQ3jIAYhMBDUBAAC6MTpmIqwIBAAAgBCJqK+fAwAAIPy23HidFBOjjOtvkn1MoUxm7oMDAHoul8Phc7i5LTde1/QjNlZpk89Xrynfl9lK+AUINs5QAUQtOh8BgD+8j5aGv103GxtV8tQT2vrj61W5elUI6gUAQOQrXvKKdtxxu1fatlk3qfzjj9qfuL5eZW++oW0zb1TxkldCU0GgByOoCQAA0E20FrAMxuPnB55cSGATANDjFC95ReUfvNeyMTUMVa36X4fKKv/gPQKbQJAR1AQAAOhBAg1yFi96UYbLFdzKAAAQoVwOh8qXvR/UMsuXvd/uo+sA/EdQE0DU4euDAOC/5r03DT9e3vFw3gx91Gu0V5rzaLlqt2wOat0AAIhU5cs/DPhO4MN5M/Rw3gz9bvBV3iMMo6lcAEFBUBMAAKCb+d7ofu7fg/qmeI1LTbT5VcZ/005qkeY4erRzFQMAIEo0lhR7DLUMbmbWl7VbhtNskbNZ2MW7XACdwee3AAAAupkJo3M0bkSWXC5DIwaleY2LsVr06C3f1f4j1Vqy5DNV1LtUZUlQXs032pyU67Nca2pqCGsNAEDkiMnIbDU90VErSUpw1ev6PW9JkuKcDXpi0OWt5jdM8oqJtlUugI4jqAkgagXj4xcA0B2ZTNKw/nZJksXc8sGc9JQ4pafEafjt52lH0UxJUq3Z5jOoaUm1K37YCaGoLroh2mgA0c4+foIOL1nc4oBmNY6/EzOzodz9e9q+f+iVfue1KMeQx7uzTCbZx08Iel2BnorHzwEAAHooa1ycbLmDJEmmdt61mXnl1TK1EiAFfOE12ACildlqlX3i5BbpJo8gp33SFCXkFzSlt1GOZ1DTPnGyzFb6lgHBwpkpgKhj4hIJAIImd+79suUOkslHTDN7VpGSxxR2XaUAAIgAmVOnyT5piteXSk1SU4/LSVOUOXWacopmfxvYbL0hPZZ6LD+A4OEWAQAAQA+XO/d+VVVUS0987pWecfMtso8ppIcmAKDHypw6TUnfv1ha8JkkyZqcrLwnn/HqcZlTNFtHtxVLr33dYvrU71+onAu+Tw9NIATYqwAAALqJzrzHMCYurkVa2qljO1EbAAC6B8+ApDUpsdUApdUW0+q0vSefT0ATCBFuuwMAAMDzyToAANAGUxsNZlvpLj6cBoQMQU0AAILAZbjCXQWgU9q6GAMAAMe11Vy2lW505jEKAD7RBxoAgADsqdyr/x5YpW3lO3WwulhOwymLyaI+iZnKsw/Sd7ILNSA5J9zVBPxGTBMAgPa19dHSttKJaQKhQ1ATAIAOKK45rJc2LtG2oztlNpm9emg6Daf2VR3QgepD+njvZ8pLHaSrRkxVZkLvMNYYPVEgvS4JaiKYuIYH0F11uKdm6KoC9Hg8fg4AgJ9WHlyj33z+e+04ultS24+cH0vfcXS3fvP577Xq4JouqyN6ts5cOPH4OUKhrZ5LABCt2g5qttVTk7AmECr01AQQtThBQFdaeXCN/rLh5Q5N45JLLkN6fsPLMiSd2qcgNJUDgoDQEwAA7Wv7Q0Gt5+eSBQgdemoCANCO4poSvbjx1U6V8eLGV1VcczhINQKCj56aAAC0zrPXeVutJR8KAroePTUBAGjHSxtfk6uTJ6Quw9BLG5fojjGzglQrIPS23Hhd0w+LVZb0NNn69FVMr3TFD8lTTFq64oedIJPZ9z1yl8Ohsn/+Q5UbN6hh+zaprtY7Q4xNSWPHKeuqa2Sx2UKzIAgJR12dvnlioRo3fN1iXGl6vpQ+SuX/+bfqv9NHsSkpXV9BAAiBNm8Culo/V9x+91067KxRzMCByrnzbsUkJISsbobLpdotm+U4elTW1FS/2mlEBkddnXYvXCDnlk2SYchsT1P///uFYhISVPL+uzr6wXtSrcc5lNUqW/8B6nfHT0O6TUU6gpoAog6didCV9lTs1bajOztdjksubTu6U3sq9/JVdIRc0A+TToecJSWqLSlRraSKj/4lSbKmpSlj+lVKHlPY6mTFS15R+Qfv+S67sUFV//m3qv7zbyXkFyinaHaQK49Q2PXAL9Wwq/1jo1Ffr9133i5TfLyGPv5kF9QMAEKrtRhh5epV2r/kbcl+dotxhiQZhhp37dLO22+RNSNTgx96JOj1qly9SiWLX5KjrMyd1l47jcjQWpvqKivV7jtvb3sih0MNO3eEdJuKBoTsAQDw4b8HV8lsCk5zaTaZ9d8Dq4JSFtC6wHsU7134WIencZSV6cCTC1W5uuV27VdAs5matWsCqge6lr8BTU9Gba223kZPdQDRr3lPzcrVq3TgyYVyVlS0NYXXkKOkWDvuvTuodTpWB8+ApuS7nUZkCKRNbS4U21S0oKcmAAA+bCvf2eZXzo8xDEkui0wWp898LsOl7eW7glc5IEicDQ2qWbtGyjvZK/1wTKqcJrMSnHVKdtaqzhwjq+GUDMmq4/tFyeJFSioY7X7EzeVweAU0HSazSmNS1GCOUb05RjaXQyYZcpisyqo/onhXgztvzdo1cjY08Ch6hHLU1XldfNWaY7U7vo/iXPUyyZDFcKnRZNW+uIwW0xq1taqvqOBRdABRx/C4aegZojRcLpUsfsnntLsSsjWqYpvMHmU4SorVWFMTlMeGm9fBIbOcJotijUZ3WvN2GpGheZtaGpMsh8miBGedkpx17vRKS7wSnXXantBPMYZDZsOlRGedejUeD6QHc5uKJgQ1AUQtXrmNrnCwurjdPA0bx8lVlaa4/H/JZKv3mfdA9aFgVQ0ImpJXF7ea/uzAi92/45z1qrPESpLsDRX68Z433RdojrJS1W7ZrIThIyRJ5cs/dE9nSHp0yNU+5z9z1+uyO6q96tPn6hkBLQtC6+AzT7l/l1mT9FTupT7zH41J8hre+5tfashvfx+SugFAqHi+Wj3OdjyMUrtls7t3pKmNq5P3M0/T+5mnac62F7zS9z82TwPvndvpunnWwZD03IALVWlN0M2733AHxpq304gMnm3qqtTh+mfGWPfw9H3LlFt7UH/P/K6+TslrdfrvH/pUJ1fucA8Ha5uKJoTpAQBog8twyWn47n0pSa6qNEmSsyyr3bxOw9luz0+g0zr4Us3G4vaD7ccCmpJUbktRndm7J6Xj6NHj5ZUcvxnQaGr/HvpXzU7W/akPwqPxcIn799rUYe3m357o/Q5hZ1VV0OsEAKEWH3u8Lbv+/OHu355tX6+Go+oIR2lp5yvWrA4Ok0WltlQ1mmNUHJveZj5EBs821TOgKUkf9j5VktoMaErSO1lneA0Ha5uKJgQ1AQBog9lklsVkCWqZFpMlaO/oBIIlJrMpID9n2wv60Z63AyrDmpp6vLyMzA5N62oWhT1WH0SemN4tHyv3JafWO0BtSUpqIycARLbn5ozXc3PGKzXp+E0+z7bP4tFTM7dmf7vlWdPT283jD886GD7uanrmQ2Tw1aa6/Pg6boKj1ms4WNtUNOGqCgAAH/okdiw4057sRII1CB0jwPdyZFwx3f27rcfnfLGmpSt+2AnuYfv4Ccfr5Mf0RrMTd8/6ILL0uelm929fF8/HNN+ecu67P+h1AoBwiR92gqxpaQFN23f2nUGvQ1ttbvN2GpHBs00NhmBtU9GEoCYAAD7k2QcF9evnQ+y5QSkLCCaLzaaE/IKAp8+YfqXXxwfMVqvsk6Z8O9R+4MszOJaQX8BHgiKYNS5OttxBkloGo1tj8rjCNsXH85EgAN2KyWxWxvSrWqa3M501IzNoH3TxrENbx+Xm7TQig2eb2pzJj7vCnms7mNtUNGGrBhC9+FIQusB3sguD9g5Ml+HSd7ILg1IWEGw5RbOVkF/QoZ6a1rR0Zc8qUvKYltt15tRpsk+aIqMD7/dMyC9QTtFs/ydAWOTOvV+23EF+bSnHtidTfLyGPv5kaCsGAGGQPKZQ2bOK/O6xac3I1OCHHglNHex273n5aKcRGY61qZ0Rim0qWvD1cwAAfBiQnKO81EHacXS3XAo8uGmWWYNTB2pAck77mYFO6uB3gtxyimZLB8qlv37Rbt4+t9ymrFEjffb8yJw6TQlTLpYWfuazLGu//hry65n00IwiuXPvV8r7G6W1B3zmM1mtGjhvAT00AXRryWMKlVQwWnrkI0lS3MBcqaTBK09Mbq5y7rw7ZL3pkscUqv/wk6XH/yNJ6n3ZFRp05ih6aEaB3Ln3Sw8v9060mDVw3gLpiVVtT2ixaNCCJ3pkD81j2LoBRB2TH4+7AcF01YipMndyuzObTLpqxNQg1QgIHYstxq98cXlD/bpQMlnb/9hW4oknEdCMQiazH+t22DACmgB6BM820ZLc8qNouffdH/rgk8f5alz//gQ0o1hsdna77ac1KalHBzQlgpoAALQrM6G3rhlxRafKuGbEFcpM6B2kGgGtC8ZbOfy9ceTvR4n8yRfoB44QXoYfWxw3IgGgSVc0dTSn6Gl4/BwAAD8U9imQIenFja/KZRitP4re7EzSLLPMJpOuGXGFCvsE/hEWoCv5G4MyghiJDGZZ6Dr+rDaCmgB6IlNrL4IxFPj7YfzkeVymZY1ufm0qNLEENQEA8NepfQo0MKW/Xtq4RNuO7pTZZG71I0LH0gfbB+qq4VPpoYmo4u/5sf89NdvPSEwzOvmzbolpAuiJWjv2uQxD5hBHoTyPy9wwRE9AUBNA1KKZRjhkJvTWHWNmaU/lXv33wCptL9+lrd+OM5nMyknqqyH2XH0nu5CPAiF8OhFJ8vvxcz+Pwv7k8rcsRBb/vn4OAJC65gae5zxcNK1RjdXnH4KaAKIOF0iIBAOSc9xBy+uXN32t8PKhF+rcwv7hrBZ6uiCcAfv/+LmfBfJOzW7Lv56atNoAIHVNz0l6anYj/rziJfS1iHh8KAgAgCDh1BHdQavvAWuFvxdLfvXU5MIrKvnTC4iYJoCeqLVDX5d8KMhzJjStUY3V5x+CmgAAAHDzu6emvwX6805Nf8tCZOFDQQDgty7pqelxYObx8+jGDV//8Pg5gKjjcjqb/q+tVemyD2QfP0FmK4czhIfL4XD/rtm0Sa78bLZHhF2nwkh+nkTv/Nk9OtpY2TRgNksWiyyJiUocla/UCy7S3kcfllFcrCpLvDRoqs+yarZuVal5N8fzCOaoq9OePz4ux8b17rTyzO9KKXk+pyOmCaAnau2GztZbZynWaGwan5KizGuuU9KJJ+noR/9SY0mxYjIyZR8/QSazWdUb1qvq8xU66HLIOmiIUs7x3T66HA4d/uA97f7kv1LaBEnSvj8/reSavZKjUXIZsvTqpYzpVyr55FEymenfFmrOhgYdWPSialb9T6qr8xpnTktX33t+psNvLVXdZ582JebN8MrTcGC/ttx4XYt0T9w4JKgJIMoUL3lFez76r9T/QrkaGnT41SU6vGSx7BMnK3PqtHBXDz1M8ZJXVL7sfWnINZKk6jWrte3jRWyPiFqVq1dp94svS1nfbzev4Rk6dbkkl0vO8nJVfPKRKj75yCNf++oP7NfhL1dwPI9Qux74pRp27WyRbvgRPudyCwCaGCa5G0WjokKH/rhAh5rlOfzqy5LZIrmcxxM//5+KX2m7fSxe8orKP3hPkuSyJklp346or5fqat35nCXFOvj4fB00m5V98y1KHlMYvIWDl70LH1PN2jVtjneVlWrvnJ/6LMOfNhY8fg4girgb7Oa9iAxD5R+8p+Ilr4SnYuiR2B4RiTrzFfHK1at04MmFctXU+Dmv4J1su8ti/4k4bQU0/UUvEgA9Uf3+fa2k+nk89AxoHtNG++gZ0GyaxfF5GG0df10uHXhyoSpXr/KvPuiQ9gKaweT085ytO6OnJoCo4HI4mnrE6fjpQI01XobHcPmy99X7B5fx6CJCznN79FRtiXf/ZntENDFcLhW//GKHpnl24MUt0iYWf67RFZvllFm/y7va77K+SsnT5OL/yiKXJPafSOGoq/MKaL6QM0X74zI6VAYxTQA9kaP4kJQ0wCtt/uDp7t8XH/xEI6p2ySWT/p51huyNlTqzdK37uqbBZNUbfc7WqUc3anDNfvd0nu2jy+E43kNTJv0nfZT+k36KO++bfc7Wmx7zv2z/cg2t2eseLn75JSUVjOZR9CByNjR4BTS3JPbX/8v+Xot8d25fJJvh0LaEfnqt74RWyyq1pephH4+eS5JRXyeXw9Gjz5fYegFEhfLlH7b6nrftCf2ODxhGUz4gxNraHlekn3x8gO0RUaR2y2Y5y8slSTFGKz1E/LQsc5wk6ZNe+R2ednG/c48PsP9EhIPPPOX+vT+2d4cDmpLUPzMpmFUCgKiQVV/qc/ybfc5Sg8mqdSl52pA8SJ+lj1KJze4e/2rfc7UzsZ9e7Xuu94Qe7aNnO7k7vo9XQLM1r/cd7zXsLC9T7ZbNfiwN/FXy6mL373qTtdWApiTNG3KlJLUZ0PRXbs2BHn++RFATQFRoLCluNf1gbC+/8gHB5O92xvaIcOlo5zjH0aPu33Guhk7P/+vkIR2e5pv4Pl7D7D/h13i4xP3b82K7IyaeOqD9TADQTdx/3ak6N6VC3yn/Wj/a87bPvE6TRbvis93DdWab+/fe+Mw2pzvWPnq2k7WW2IDq69n+o/Mai4+/JbXRHBPy+Z1f/FmPP1/quX1UAUSVmIy2G/ZA8gGdwfaI7saamhrU8lxtPHM8Z9sLLdLaerSK/Sf8YnpnqHHf3vYzynvdxvTtq0G/ejBU1QKAiDWwT7KS8+J1+AuXshrKNGfbC222c4a828s234HZzLH20bOdDPQ918Fu/3u6mMws1W5YL8m/DyUGovm5VE8/X6KnJoCoYB8/of0Xc5lMTfmAEGN7RMQK8Aw6ftgJstjtQaxGJ1+kyP4TEfrcdLP7t6kDG1e/u38WiuoAQFToSPvl2V761XZ6tI+e8wmk+bfY0xQ/7IQApkRbMq44/t5Uf4PUncL5EkFNANHBbLXKPnHyt0OtN9v2iZN79EuS0XW8t8fWsT0impjMZmX+0P8P+7Sns70T2H8igzUuTrbcQR2axpySIlsS79EE0HOZrVbZJ03xK29HbwJ6to9e8wkggJb5w6v4SFCQWWw2JeQXSArCDV4/cL5EUBNAFMmcOu3bhrtZA2EyyT5pijKnTgtLvdAzubfH5ieRbI+IUsljCpU9q0jmxMROl2WYfJ9iJuQXsP9Eidy59/sd2DSnpChv3oIQ1wgAIt/x6xbfPHvzuYNgZkvLjG20j8fm06GbiWazsmcVKXlMYUemgp9yimYrIb8gZI+fH8P5UpOeHdIFEHUyp05T/ZmTpb+sliQlnHSy8q66vcffoUJ4ZE6dpt4/uEx69BN3Wt6Tz7A9IvwC7ByQPKZQSQWjpUc+CnjWKWedI+OQTXK2PJ1PPvMsZf7wallsTR9DYP+JDrlz79ee1Xukf2xrO8/8hfTQBAAPrZ0neup9/U2K32WRdpVJklLO/p7yLhnf1HvSox3OnP5DpZwzoc32MXPqNGXk7ZU+2NJunfrc9hMlnzyKHpohllM0W9biCum5VW3nefhR6dl1HS477QeXqdekKZwvfYstGUDUMVuO372MH5LHAR1h1Xz7Y3tEtOvshU6fGde12Tsh+9rr3QFNif0nmphjfH/FlYAmALTkq11LOXmU1xMLiQVjZLZaW7TDvSf7EcDys+1OOSWfgGYXMcf4XmcJvXsHVG7G9y/kfMkDWzMAAEA3EepHnfzlcoW7Bgg2I2K2LgDoHgxJLsNoloLuwmB1dgmCmgCiT1d8SQ4AEDAXZ/LdD6sUAILOs7l0deI4S7sbeVgnXYOgJgAAQDdj6oIvbgIAgE4wDBkegS+jE0EwfyftzDyASERQEwAAAIBPXAYDQPC5PLtnduZA62ewkmN51+lMz1v4j6AmAAAAAABAFzIkeb6CunOPn/s5T3pqdhn+1l2DoCYAAEA3wfkzAADRI3iPn/vZU5PzhK7D37pL8B14AFHH801xVeu+VGnNFtnHT5DZyiENXc9o9pnn6o0blHDCcJnM3DdEz7Tl5hukQVcFNu2N10lmc9O/Y1deTmfrmW02We12JQwfqYzpV8piswVW4S5iuFyqXPelDv2/12QcPNC0fFarFBcnGSapvrZpWc1mmVJTFZOVLdeREslsUezgIWooOSTn/v0yWWMUO/JEOY+Wq377Nsnpkslub7qgPXK45RWr2SKZTTInJCj5exNksVpUt227XPV1UmKSGg7ul6u0VGpokCyWpn8NDcf/7mazZI3RwcRBUq+xXf+HA4BuavtdP1Ft9jlSXIYk6eiXa7Xz3XVq3LJJyr3SnW/DjddLLpcsKSnqfdUMpZyS7z7PdDY06NDiRTq8o1qKG9HuPHf/7mHZ8/OVctY5OvL/XlP13m/k2r9PRkOj5HS0aEMsWVnqf+//yZaUFLwFD5HGmhrt+f0jcu7e1bmCTCZZs/oo9fQzlXbexBbXmI01Ndr7h0fVuGuXZLhaL0PSodh0qf8FbY7fcuN1Ut6MztUVBDUBRJ/SD96TlC5Jqt+5Q4e/WKfDSxbLPnGyMqdOC2/l0KNUrl6lQy88L2Vf5k7b9/tHZE5MUtaM65Q8pjCMtUNPZgrnd4LaCkK2Ytudt0uZl3gnulxN/9rT0CBHcbEqiotV8clHSsgvUE7R7I7VtYtUrl6lA0890XK5Ghub/nlyuWQcOaKGI0fcSY4D+72y1Hz2qdewUVLc9sxdTskluSoqdPTNN3xXtLV153JJDfVSnP/rFQDgH5dHd42qFf9RY9XulpkcDkmSs7xch/64QIdMJmXPvFVHV/xHNWvXNI1LHSHFtT+/uu3bdWTbFh157VW/6uc8dEi7flIkc0qK8uYt8GuacNhx791y+GoLO8Iw5Dh4QEdef1VHXn9V9klT3NeYHZoP3WK7BN1IAESV4iWvqOLTf7ccYRgq/+A9FS95pesrhR6pcvUqHXhyoVzV1S3GuaqrdODJhapcvSoMNQOiw7Y7b5eroiJo5dWsXaO9Cx8LWnnBcuxY4VegFgDQrbR3Lmh4BDUN+XlH0jB04MmF7oCm1NTh369J/Z1HM66KiqYbkREoqAHNVhy7xuzofIyw3mHuOQhqAogaLodD5cve95mnfNn7cn17NxMIFcPlUvHLL7abr2TxohaPpwM92bHjc0NVVVADmsfUrF0jZ0ND0MsNlOFy6cCLfw13NYKC/iYA0DGGy6WSxS/5zmMKIKjZWjl+TtuZY7mrokINVVWdKCH4GmtqQhrQPKb8g/c6PJ/OrE/4j8fPAUSN8uUftujG/2mvfJ1Rtu54gmGofPmHSp84qYtrh56kdstmOcvL283nKCtV7ZbNShje/juOgOAITujJbDLJFeBjUw/7eD/UsePzvkce7PC0bUlvOCqrcfzRaMvjy2VNS+9wOaHgqqtTo/1syd40XBzrX73sDRUqt6VIkrLqjujU8g36e58z3eOLdr6qhYOu6FBdTitdp7NL1+qDjHFak3pCh6YFAHRc7ZbNcpSVSb1aH//HQVO9ht/uc6be1pkt8h1rG6/c+74G1LUMrC3qO1F7Evr4Vac/DLlSd2/7mwxJv8u7pt38mfWlXsOmBR/K1ifbr3l1hYZDh2R8+95KQyaVxKYFrew5215oNd0lkx7x42/XnkDOedASQU0AUaPx27tjSc5av/IBoeI4etT929nOXVjPvEC0uOycwVryr+1BL/fY8TmY+0WpLbXZTCQVR1BPEj8DmZ6OBTQl6VBcL6+ApiQ9O+DiDpe5In2Uzi5dG5KAZq6pMuhlAkC0C/Y54KKcya0G2vwNaB6zNy5T9eYYv/K2ejMuktpYU6IUmxiSoo9aE5XqaPmaqW2JOSGZHwJDUBNA1IjJyJQkxbl8P1p4LB8QKtbU40EUz8eGBlfv85kXiBaTxg7QibnpqqxpVN/eiaqtd+jTdQfUt3eiXIahvSVVOrRtj1zf7FLvhnIdiUnV1ylDWpRzbJ8YXLNPJ1buUMzopo9qWVNT1djK+2gD0a+2WGeUfukeTjglX+kTzgtK2Z1V980eHfZ41/Mr/YJTrzpLbFDKCVR6w1GNqNqlnQl9Napim84s6B/W+gBAJDp2DnjH9pe1LGOs1rfSTgbblOLPdCC2lwbUHlKyo0bVljhtS+zv1UY7zBY1+BnUPL30S+XUHu8wYu3VS32uvT7o9Q7UocUvqXF/08f0PuxdqMNB7KnpNLX+tkZ//3aeRlbuVLUlTrsTWu/lmtZQoX51xRpSs08jzOXadsWd+nD1XiXGWZWeEqestATV1DVq2oShHZ53d0dQE0DUsI+foMNLFvv+kpzJJPv4CV1XKfRI8cNOkMVul7O83Ot9OaZmj/5a09IVP4zHPBF9zCaTBmQlu4fTkmN1xfg8rzyucwZr28wb3cOtBTWvOPDh8QGP43O/u3+mXT8pCkpdMxvKNKj2gHt4yIxfymKzBaXszjIG2rX1jT9Lld2rJ+OP97wpSTrz22By5rT/L5zVAYCIFD/sBFnT0qSyMl1Y/J8uCWqeUrFNp2ibV9rw6j2tttH+6FN3xKuNzf3pfbIlJXWqjsE0bPYN2nn7LZKkrPrSoAY1gyXBUauLDjV96HZJ9nhtb6Wn5817lrp/D3h0vobZ7Tr/OwO7qopRjQ8FAYgaZqtV9omTmwbaCGzaJ06W2cr9GoSWyWxW5g+vluT7JeAZ06+UyUxTi64T4GswA2K2WmWfNMXv/J7HZ1tSkswpKe1M0XEJ+QURE9CUmo4V2VdfG+5qhFSk/c0BIFKYzGZlTL8qbPO35Q7qdBmeN+zNKSkRFdCUpJiEBFm74Ck9+6QpXTIf2WyKs9tDP59uhCstAFElc+o02SdNadEjTiaT7JOmKHPqtPBUDD1O8phCZc8qkimx5Xt8zElJyp5VpOQxhWGoGdB1jh2T29Pa8Tlv3oKgBjYT8guUUzQ7aOUFy7FjhbrhDY5I/ZsDQKQ41gZY04LYg9BkUvasIiXkF7SZxT5pinLn3t+hm4+tzurbay5zSory5i3oVFmhMvihR0IScDzWceHYOUyg83F3f7DZ5PODjjabhj3xdIfL7+nozgQg6mROnSbL7/4lh7OpUeh9xQ9lHz+BHprocsljCpU7cpT02KeSJFtmpvpdebcSThhOD02Elcnk+wNWwZQ5dZp6/+Ay6dFPWoxLv/RypfvoQZ83b4H08PJOzd+W3VdDfvl0RPcWTB5TqKQ/PSs98lFY65F68Q+k9Z0vJ+Wsc5Qx/cqI/psDQKRIHlOopILRQWkDsm69XSmn5MtkNit5TKGcDQ3SvE/d43tddoXSzpvobndba6OTzzhLyUNHSh9sa1F+cxa7XbkP/iziemg2N/ihR5S8dJ206XDQyrRPnKK8C8/zOocZ/NAj2rd6t/QP/z+maLbZNOSJpvOUhMVfSLvKW+QZ8Oh8emgGiCsuAFHJ84I9feIkApoIG89tMTa7rxJHjCSgiR6nrWNw7/MvCPnxOXH48KgIrkXCcSHrwo5/Nb01fWZcFxV/cwCIFMFqA1ILRnuV1fxY3GvK+S3a3ebDKYWn+n0M7/ujGyI+oHmMyRLc8w37WWe1eg5jie1Y+2dOiHf/vS1tnBMR0Axc+M+uACAAXdgJCfDJ1YXvMAQAAAC6isnHu+PhH/6CoUVQE0BUMhPVRIQwPL7MwmYJdD0uuAAAPZURwBcCO9Rq9uAmNlgfX+zKVwL1RAQ1AUQlGgdEiq782jSAVtAcAAB6qEDOQzsyCU1s53letnIJG3wENQFEJTMNAiKE54khAU4AAAB0FVeITz655Gqpo39y/oahRVATQFSipyYiRSCP/QAIHloDAAD817HHz3tuKxu8M/ye+zfsCgQ1AUQlemoiUhDTBMKM9gAA0EMFcnO9+z5+3jUn5R3vHXs8P9cNwUdQE0BUMhHVRITwPJmk1ybCjU0QAICewxXidr8Hd9RsU0fPtbxfVcWJWrBZw10BAAiEZ0yzdNkHso+fILOVQxq6ntPhcP+uWveltnzwBykuTkmFY5V15dWSpH0vvqC6lZ9LLkMxmZnqd8/PZI2LU/nyD9VYUqyYjMw2t2FnQ4MOLV6k2k0bJKdL8UPylHL6GUocMVImM/cmATNXXH7bMvNGKffKcFcDABCgLTde1xRp/Lbta4iNl/pd1qEyDr3wF9UYFil+VAhqGE7BPR/Y9fOfqbrhaIv0Qyl5UuZ3/S7HM45JSDP4IiYCcPjwYf385z/XihUrNGjQID344IMaPnx4u9Nt3rxZF110kVfahAkT9MQTT7TI++abb+qxxx7T8uXL3WlvvPGGHn/8cVVXV+uKK67QHXfcITMXiUBEK17yipyl8VJMkiTp8Ksv6/CSxbJPnKzMqdPCXDv0JMVLXtGuDz+RcpudTNbVqerTT1T16Sctpmk8sF+7flLUIr21bXjvwsdUs3aNV76qI4dV9b//yhQbpz7X36jkMYXBWRh0K8T50CqPmzAAgChlGO5ImVFX7zVq78LHlFM02+fkjtIjcppjpfj2Z9WTv2NgtBEkbSvdrzKJagZdRETvDMNQUVGRSktL9dprr+maa67RLbfcourq6nanXbNmjU477TStXLnS/e/RRx9tke/gwYN64IEHvNI++eQT3XfffZo1a5ZeeeUVrVy5Ui+++GLQlgtA8BUveUXlH7wnU/P7XIah8g/eU/GSV8JTMfQ4x7ZFz7vCnTpPabYNtxbQ9MpeX6cDTy5U5epVnZkrAAAAopTRLOhYs3aN9i58zCut+fVRR85XazZvCrRqUS8UQU0EX0QENb/44gutWbNGDzzwgIYMGaIf/OAHGjRokP75z3+2O+2aNWs0btw4paSkuP8lJCR45TEMQ/fee6/69Onjlf78889r4sSJmjp1qnJzczVnzhyCmkAEczkcKl/2viTJ1MZtrvJl78tFTxSEmMvh+Dag6X1iuCMxRyU2u+pN3g9CuGTS/tjeqrB4t0815ljVmWO80sqXva/GmhqvgKbDZFatOVaHbGktTkRLFr8kw+Xq9DIB0aoHdyLpsP2xvcJdBQBAJ+yK76M9cVk6aEvX7vgs7YvLaJGnZu0aORsaJHmfsx5TbEvX0ZhEv+Z39MNlPfY8c2dCXx2I7aUDsb20Oz5LOxL6al9sb+2Nz+xQOV7v3+cB9KCLiMfPN2zYoL59+yovL8+dVlBQoC+//FIXX3yxz2m/+OILff3113r66acVGxurKVOm6J577lFcXJw7z4svvqj9+/fr3nvv1a9+9Suv+d59993u4ZNPPln79+9XaWmp0tPTA14eqzUiYsVBZ7GYvf4HutqRD5e7++xbDac73SWTzMcaCMNQ5cfL1WvS5HBUMWqxf3fMkQ+Pv8akODbNa9yfBzS9EuWK/f/U4Jr9kqRH8q5xj7/g0Kc6qXKHim12Pfdt3tk7XlG869vHhwxDBxb8wZ3fJZMeHXK11zzmbHvB/dtRVqaG7VuVOGJEEJYM0e7YybLVYnafj4Rz/+6Kc6LYGEu3PfcKthf6fz8o5fD3jhy030DPsrjfRL/ylb72irJnXOt1znrMx71H+z0/V0VF1JxnxsVaglreR73HBKWcOJvV3W7G21oPwdGuBi4igpqVlZUaMGCAV1pqaqo2btzoc7ry8nJVVVWpqKhI5557rr755hv99Kc/VXx8vDtYuXPnTs2fP1/PP/+8amtrW8x34MCB7mGLxaLExEQVFxcHHNQ0m01KS/Pvrke0Sknx4+UbQAiUHi11/x5Xvl7vZJ0hSaq1xCrRWeceZzpa2u33w1Bh//aP57a4NmVYq3n+1Wu0O6jp6e9ZZ+ikyh36Jj7LnbYzIVsjq3a5h53lR9y/ay2x7dYn1lHLNg9JTS9DMCTZ7QlKS4nzGtfV+3def7tf2+W8n5ylO+e3fP+sv344eYSSEmwBT9+VzKbQf6k21CaflsvxJgLRfgORb861p+ql9zfpm0OVLcadXZCjXQeOavfBluMC4SotUVpaovucNdFRo2prQjtTtdSn/kjUnGfOvOwU/euLfeGuRgs/+9FY999v9g9H6+r73/ca//CtZ0TF3zdSRURQ02q1KjbW+6ItLi5ONTU1Pqez2+1asWKFezgrK0tFRUWaP3++7r77bjmdTt1zzz26/vrrNWrUKH3++ede01ssFtls3ifBcXFxfr3Lsy0ul6GKCt/1jlYWi1kpKfGqqKiV09kzu6AjvIzU4zcb7I1VPvOVlQW+H/dE7N8d47ktNn+XUUDlNXs3j8XeS42HS78d1756azzbPCQd317Kj9ZIzqYe7V21f78w99wWaf5sl72TbK1O66/G+kaV1TcGPH1X+st9LZezeuNG7f7tQ+7hh/NmBFz+7B2LFe9q8EprNFn0+yFXtcjr2eNbkgbec6/fPXE43kQO2m8geozsn6rf3DTOr7x7HpuvqjVfSJL2xmXoxZwp7U7jeVw3p2eorKzafc56267XJHm3MdP3/UO5tQf0WdrJ+qRXQavlSNF1nhnI+URn2uHmf6usH17Z6hODnn+/QM+XepqUlHi/nkKIiKBmWlqaDh8+7JVWVVXVIuDoj8zMTJWUlKi+vl7PPvuszGazZs6cGfL5enI4uvcJhdPp6vbLiMiUfPZ4HVr8smQYLT8UdIzJpOSzx7ONBoj92z/JZ4/XoZcXSQrOy8JdnoFRk0nZt9+hnbff4te01rQ02YYMZb3Bi9PRcl9m/45MtiFDZUpJkVFR0emyWjsatfUOak8WO8eRaMf+DXQvmTf8WFVFTXGMNq97fEi/fJocDpfXOWtL7ZfbE84zbUOGSklJUlXbnWb8wnVoWETEg/v5+fnaunWrKjxO5r766itlZ2f7nO7jjz/Wrbfe6pW2evVq9erVS7GxsXr99de1ceNGjRs3ToWFhZo5c6b279+vwsJCrVq1Svn5+Vq9erV72h07dqiqqqrd+QIID7PVKvvElne+PJtj+8TJMlsj4n4NujGz1Sr7pKY75sF4ktQzMGqfOFkxCQlKyC/wMcVxGdOvkskcEc05gACYzGb1uSrw3pnejh+RbLmDmsr3Y6rMH3IcAYBIYo2LU9ygb4/jHTzZTMgvkOXbjlqe56xt8VV8TzjPNJnNyr7muk6Xw3VoeETE1jl06FANHjxY8+bNk8vl0vr167Vs2TKNHz9eLpdLFRUVcjqdLaY78cQTtWLFCj399NPatGmTXnrpJT3//PO65pqmDzIsWrRI77zzjpYuXaqlS5fqgQceUGZmppYuXaqTTz5ZF110kV599VVt375dTqdTjz/+uE455RT17t27q/8EAPyUOXWa7JOmNLtIM0kmk+yTpihz6rQw1Qw9zbFtMWiPnzfbhnOKZvsMbJri4pQ9q0jJYwo7PX90I1H+vsaeKnlMobJnFUlBuhhKyC9Q7tz7lT2rSCarjw8nmM0cRwAgQg2+/5dKyhuijjTuCfkFyima7ZV27Jy1I3raeaa7HQ7wvJ7r0PCJmDDyQw89pJtvvlnvv/++KisrdfHFF+uss87S3r17NWHCBC1dulQjmr3np3fv3vrTn/6kBx54QAsWLFC/fv00d+5cTZ06VZLUp08fr/z79u2T1WpVTk6OJOncc8/Vp59+qosvvlhJSUmSpOeee64LlhZAZ2ROnaaj486TFq2VJKVfdIkGTJrAnTF0ucyp0xRXv0ra2/KxUUtikoY88XTTwLxPvcblzl+oPRuOSMu3S5ISTx2rvCvmtNiGc4pmK760Snr6f17pfe/4qRJHjOz2d87RCUEItqNrJY8pVFLB09IjHwVcRsoZZ6n/9GnuHjrJYwo1NL9A+t3HLfL2ue0nSj55FMcRAIhgp/z+EdWv2SW9+GW7eYc88bT7+N9c5tRp6v2Dy6RHj3+YL3HUKRpy7a+08fNvpP/sdqf31PPM5DGFSnrqzx1qh9N+cJl6TZrCdWgYRcxffuTIkfrggw+0cuVKpaWladSoUZKknJwcbd68uc3pxo4dq7feesuveYwbN07Lly/3SvvFL36hq666Snv27NGYMWNkt9sDXgYAXcfs0fMk9axzaEgQNm29U9Oalt7miaUtKUkmS7l7OGHEiW1uwxZbTIu0pBNP6nhFAUS8zl5AZk37oSw272OJuY0yU07J79S8AABdI6bZR5Xb0tZ55zHNzzXTJ06WxWaTyeTdTvTk88yOtsMZ378wRDWBvyIqCpCQkKCzzz67y+c7dOhQDR06tMvnCwCIfoYfH+HoijIAoLUOuiZ67QJAdAvRYZzWAd1Bz+pPDKDbMNEMI0IEIx7p8lEG8U50BJtLz0bbCADdjznEN6c4d0A0I6gJICp5tu30ckM4tbf9+bN9unzkYfsG4DdimgDQ7YSqx/2xYjnXRDQjqAkAQCf46mUp+dfTknNJBBuxrZ6J9Q4A3Q9vEQHaRlATAIBOaO/utq9emO48PiKj/kwPABIXvgDQHXFoB9pGUBNAVPJ+/Dx89QCCsf0ZPt5mxPYNwH9c+gJAtxOyx8+byuVcE9GMoCaAqOcrIASEWqDv1PRM91UEWzcAf9FTEwC6Hw7tQNus4a4AAATE43Hd8k8+kS0jWaaERB39YpVqVv5PamiQzGZZMzKVNmmyYjMyFZc3VHXbtspx9KisqamKH3aCTGbu7aBz2np0vH7fN9py4xNqNFulwVd6jdty43U6lDpcyhgrSSp55+/a+e5+pZx2utLOmyiXw6H9Tz2puu1bVeaySTkXeU9/0/WS4Wq9QmazzIlJihs0SH1+PEvWuLjOLyQ6zNnQoOJXXlb9rp2yJCQobdIUJY48sdVjjrOhQQdffknVa7+Qqqslq1WmpGQp1iajpERyOPyfcd4MSdLBRX9T4rXXyGKzBWuRAABAGIT6Bjc30BHNCGoCiDqVq1dp/5K3JfvZkqTSd96Wy1HVMqPTKcfBAyr56/NNwyaTV5c4a1qaMqZfpeQxhV1RbXRDxUteUf2BGMlmbzny222t7V6Yx++7u+rq1Fi2T0def1VHXn/Vu5gYSytltxHQlCSXS67KCtWs+1I7imbKljtIuXPv970gCKq9Cx9Tzdo1Xmm1GzfIFBOjPjfe7HXMaS2vGhpklB7pVB1qVv5P2z//txLyC5T7kzs6VRaiBz01AQD+os1Ad0AXJQBRpXL1Kh14cqEcFZUdn7hZdMlRVqYDTy5U5epVQaodepLiJa+o/IP32s3XVkzT8HwvrI8Hi3yN80fDrp3a9cAvO1UG/NdqkPJbRmOj1zHHV95gqVm7Rnse+0NI54HIYeIhRQDodkJ/ZKevJqIXPTUBRA3D5VLJ4pckSSaPxrfWEqudCdkaWv2NKq2J+mv/70uShlXt1hmlX6osJkUnVO+RJO2Oz5LJkAbUHXJPX7J4kZIKRvMoOvzmcjhUvux9SVKNpfXHu0ti0/Xwt48CN9c8/dNe+TLJ0OllX7nTPuo1Wv9NO8mv6ZsbU75R5x1e6R5u2LVTjro6HkUPMWdDg1eQckNSrnbFZ2vC4VWKNRrd6cWLX1L8iSd55a2wJujZARerwRwjSbphz5t6rv+FMkydPy5VrVkjZ219p8tBFCCmCQDw07EbYXwoCNGMK3gAUaN2y2Y5ysokSfGu4xfof+3/fX2QeZqWZYxzBzQlaUvSQD034CK9kX2O9sX2VoPJqpf7TdKinElqNB1/pNdRVqraLZu7bkEQ9cqXf+g+A6xtI6jZUf/uVaDqb8uqtsS1GdD0x2r7CO2L6+2VdvCZpzpVP7Sv5NXF7t/Vlji91ecsrUsdqv+kj/LK5ywra7E+nsi93B3QlKQ/D7i4UwFNo9kzZTv/+teAy0L0IKYJAN1PnK2VVxE1E+tHnuaSE5rOO/qkJ3R4WiBSENQEEDUcR4+6f6c4alqM35I0sM1pD9vsXgGDRpN3R3XPsoH2NJYUh6TcWnOsJKnKEt/pskpsaV7DjYdLOl0mfGssPt4D3HMd/i/txJZ5u3h91B040KXzQ+DmXDXa/fui03Pdvx/68Xf0k6mnuIe/V9BPkpSXkypJGjsiU6Y2XpA2Y9IJXsN3XHFKq/kAAJEnPSVOZ+f39Znn0Vu+61dZ917d1MaMG5mlrG+Dmaed1EffGZklybsN6qnuv+5Uv/I9+OPvhLgm8AePnwOIGtbU1E6W0PazFZ0vGz1JTEZmq+lztr3Q7qPh4RLTOyPcVej2YjKzVLthvSTvV2S0mrd3hhr37Q1ZXUzNniWLy84O2bwQXMP62/XcnPHu4UvOHOz+nZWe4DXummbByracU9BP53wbBAUARJ9rJw/XtZOHd7qcoTnebYwkmU0m/fiiE/Xji1rehO2JBvZJbvE3QuSipyaAqBE/7ARZ09Laz9gKk4/PrVjT0hU/zL8LQ0CS7OMnhPSTke0FxALR56abg14mvGVcMd2vfJa0tC5fH4OuvbZL5wcAAACEGkFNAFHDZDYrY/pVQSnL831zGdOv5CNB6BCz1Sr7xMnhroZPnmFRW+4gPhLUBSw2mxLyC9rNlzn9Klnj4vzKGwxJBQWyxMd2ybwAAACArsJVPICokjymUNmzigLrsdmsZ501LV3Zs4qUPKYwSLVDT5I5dZrsk6aEuxrtsuUOUu7c+8NdjR4jp2h2m8FKU0yM1zHHV95gScgv0IDZd4R0HgAAAEA48E5NAFEneUyhkgpGS4985Pc06VPO1+DvjJCe+K8kKXtWkbJGjaSHJjolc+o06eHl7uHeV/xQ+iLw8uwTJyvvwvNkO3hUevHLTtXN1q+/Bj94Fz00wyCnaLac+8qkv61xp/X9yV1KHHlii2NOTtFsORsapHmfBrUOCaeO1ZBrr5HFZgtquQAAAECk4GoeQFTqaDAyNruvTNbj93Hi84YS0ETQpU+c1Knp7WeeJbPVKqut848K28dPIKAZRhZbjNdw0kknt3nMCUXgMfsqApoAAADo3riiB9BjhO6zLkAEMoL/sSFED1MIP2QFAAAARAKCmgB6BJPJ+yKfcA8iUTC3SxcbOQAAAIBujKAmgB6JTmyIREYQN0wXG3lY0U8SAAAACC2CmgB6BBMhBkSDY3HIYGyuxDQBAAAAdGMENQH0DKamR9CBSBbM3pX01AQAAADQnRHUBNAjEM9ENAhmHJKgZpiF+S4KN3EAAADQ3VnDXQEACITL4ehQ/gPPPKWEmn3S4OmSpO0/vUMpzhpJkik+QTm/fEDx6emSJEddnQ4+85QaD5copneG+tx0s6xxcUGt+6F3/67K996RHA7JZlPa5PPVa8r3ZbZyWI4mzbfDjm6Xze3+9f2qry9Vic0uDbioU2UdfvcdbXlutWQyyZyYpNjsbKVNPl+JI0+Uycw9zdY46uq054+Py7FpQ1OE2WyRtVcvJYwYqYzpV8pwubT/qSdVt3O7VF8vOZyS4Wqa2GSW4uOb7qDU1anEkuy1DrfceF1TpNFslmyxkqNRamw8PvO8GV26rAAAAEC04+oZQNQpXvKKyj94r0NBAFPzFwx69GIyamv0zd13SlarbDn91bBrp3tc47692lE0U7bcQcqde39nq3687p7q61X25hsqe/MN2SdNUebUaZ2eD0KveMkrKl/2vjTkGnfatpk3dio4ZXy7YQajj6WrukZyOpt+Hy1X7dFy1W7aKFNMjPrceLOSxxQGYS7dx64Hfum170uSXE45SopVUVKsik8+8l2A4ZJqqo8PW1rLYzStk9qazla3XXTUBAAAQHdHVw0AUaXVoKCf2g0UORwtgxrfati1U7se+GVA8z3Gn7qXf/Ceipe80qn5IPTc6zLIj3gHszSjjaiW0dioA08uVOXqVUGcW3RrNaDZSTz8DwAAAIQWQU0AUcPlcAQc0GzOCKAfU8OunXLU1QU0v47UvXzZ+51+jBmh43I4mnpohsDxnpqd72fXXhnFi1+S4XJ1ej7RzlFXF/SAphScdQgAAACgbTx+DiBqlC//MOBp3+pzltfwk7mXuX9PLP5coys2u4cPxPbSX/t/X5J0yYGPNLx6j3vcwWeeUs5tszs8/+Z135CU665TTm2xrt7nESQzDJUv/1DpEyd1eD4IvfLlHwa9h+Yxf+t/ftDK+nevAv27V4F7OLO+VNP3/UMJrnpJkrOsTLVbNith+IigzTMaHXzmKfdvQ9JvW3l9wJxtL7h/f5yerxXpozo8n4e7/J2ZBFUBAADQvdFTE0DUaCwpDkm5yzLHeQ0fC2hK0tLsc7zrcLgkoHk0r7tnkHVvfGa7+RE5mq+bGFej13BKY1VXVsdvxbHpWt57jFea4+jRMNUmcnju06UxKe3mDySgGQ6xNk7xAAAA0L3RUxNA1IjJaBn86/I69M4IbLoO1j0SlhWta75uRh/drM/TTlJh+QZJ0vjDq1oEw2/e/YbsjZX6sHehtifkqMx2PHh23Z6/6y8DLmhzfnnV32j00c06GJuuXg1HZR13ptabMzQyN10HS2tU1+DUN3uPqK6sXP3qSjSg9pDezzyt1bKqLfFew9bU1I4sercU0ztDjfv2SpJcpsgNBBYc3azDtlQdiUlVZmqs9tZZNXZEpkor6rTjQIWsZrP6ZSTKZRi6/vwRsvCFewAAAHRzBDUBRA37+Ak6/OrLYa1Dn5tuDmi6DtXdZJJ9/ISA5oPQs4+foMNLFrsfQT/27kSzj0fS0xorJUnnHl6lc7VK/+xdqFX2kZKkPg2lPud3+YF/SZIG1+yXTCblXTFeU6zezbfhcmnrnbdLVVVyydRmUFOm448kW9LSFD/sBJ/z7gn63HSzdhTNlBQZH/eZtet1r9djSJLV5dCkks+bBkwm5T35jMxWTuEAAADQs3EbH0DUMFutsk+aErb523IHyRoXF9C0Ham7feJkAhYRzGy1yj5xcot0UxeExNraNkxms7Kvua5DZWVOv0omevPJGhcnW+4gSZHxcR9TO+9r5fgAAAAANOFqBkBUyZw6LXSBTavVHdxozpY7SLlz7+9U8f7U3T5pijKnTuvUfBB67nVpaiUI1sYjzKaYGGXPKlL2rCKZYjseHG9v20geU6jsWUWt1+lbhkc9kscUdrgO3VXu3Ptlyx0UGUFNH8Fxjg8AAADAcdzqBxB1MqdOU+8fXCY9+knQyuz/yDzFp6c3DTy83Gvc4IV/CriHZnNt1d1+0Q/U+/zv0wMrihxblyte+JdULMUPH6G8a25R+ZbD0lsbvPL2/cldShx5ortnZMqRZOmLfe5x+nvbH6BKu+RS9Zp8vl/bRvKYQg198hnp0Y9bHR87YKDyfvEUPTRbkTv3fjn3HJYWrWsxLuWsc5Qx/UoZLpc0/7OQ1sM2bJjU2CzRYlHen57l+AAAAAB44KoGQFQK9sW9O6DZimAFNI9pre69L7yIgEUUMlutih2YK0mKzx3UtA5b6SmZdNLJ3oFEjzxJJ53scx4ZF3Rs2zBZ2m7aLUlJBDR9MMfYWk3vM+M6WWy2oB8LWjNg5q0t0kwWC8cHAAAAoBmubAAgAhjtvEcPUaAjTy6HaXWzmfkWCX8fH28PAAAAAOCBoCYARIBICKag6xgR8Z1tNBcJ68VMVBMAAADwC0FNAIgA9NSMfqYI+MiM1LEOo/DGbggAAABED4KaABABCKZEr0DWXbhWN8Fz3yLh72MmKg0AAAD4haAmAESACIilIEDHHlk+9tSwX+syhOvbxOPLAYuE/ZD1BwAAAPiHoCYARABXJERTEBT+vJcxfD01wzTjKBEJPTUBAAAA+IegJgAAnRFIHCxMwTNCdr61tVq6MtjJh4IAAAAA/1jDXQEAiARbbrzu+EDejGbjfiTFWGUbeoKShg+Xs6xMMRmZso+fILPVv8Oo4XKp6uuvdOidt+XasV0aco3X+K23zVKc2VDCqWNly+qj6v99Ljmdij9huDKmXymLzdbZRUSIdSQWFa7gYs2Wzdpy4wKZ4hMUP3So+vx4lqxxcWGqTfg56uq0e+ECOTdtkCR9E58l9ZvUIt/Wu++U6uqaBvpNDWmdiGkCAAAA/iGoCQDtMqTGRjVs+FqlG752px5eslj2iZOVOXWaz6krV6/SgWf+JDkcPuZgkhyNqlnxmWo80hsPHVTFJx8pIb9AOUWzO7sgCIEWAcoo6A5p1NaoZt2X2lE0U7bcQcqde3+4q9Tldj3wSzXs2umVZrTx7XhXWbnMXbRieacmAAAA4B8ePweAQBmGyj94T8VLXmkzS+XqVTrw5EKfAU2p7WDKMTVr12jvwscCqia6iv/BqEh6dWPDrp3a9cAvw12NLtVaQFNqfz/sCsQ0AQAAAP8Q1AQASbvis7U7PksHY9NbjNsX21v1pqaO7U6Z1GDy7uRevux9uVoJWhoulw6+9IJXWo05Vrvi+7TIuz+ut7sfmCFpf2xvOZodomvWrpGzoaEDS4UuYfgc9G+iLrI3PkvfxGWqLCbZK71h1045jj1e3c056uq8AprVljjtjctQsc2ub+IzW53msC1VR2JSVGWJD3n9CGoCAAAA/uHxcwCQtLjfeW2O+1v/8yVJN+1eqrezztDRmCTN3PWGYo3GpgyGofLlHyp9ove7+Gq3bJZRUeEeXp16gv6RMa7VebzWd4Ikac62F/Rbj3d63rPtBa++YyWvLlafq2cIkePY186PrSeLuf2oVIzVEsIa+fZSzmRJ0jmHV+s75evd6QefeUo5t3X/VxwcfOYp9+8jMSl6ZuAl7U7z3ICLQlgjb6ZWeovG2cK3vQAAAACRip6aAKLWD/Ji2hz33dJ1QZ/fmtRhOhjXW7WWOB2I6+U1rrGkuEV+x9GjXsNtBTR9aWzWK7Sx+FCHy0DXONbDbvSwDK/0878zsEXeC0/PVf/MJP1wwlBJ0pXnDm21zDNOzg6oLvGx7QfBPuo9xmu48XBJQPOKNp7L+WHvwjDWpHXmZkHx7F4J+snUU8JUGwAAACBy0VMTQNSacFJvnfD+b93DD3/bw3HU0a06q3StzipdK0nqfcUPdfjVl1vk6yhf79uLyWj52Ko1NTWg+XjPs9l8MrM6XSaCq/k6slrMem7OeJ/TpCTY9Mvrx7qHzy3sr3ML+wetTn+842zt/PnP1Lh/vyTpP2kn69+9CnxOE9M7w+f47iKmd4Ya9+3tdDlztjW9WsJhMuvRIVe3SJek2CF5qt++zT3seew548hanVF2/OZLyjnj3b2w29t+AAAAANBTE0AUix92gqxpaS3SPUOP1rR02cdPkJKTW+TrHI+5mExN82ilfqaUlE7NxWj2gr2MK6Z3qjz0HP3u/pn7tz+vaexz082hq0wE8VzOYLy+0uTj9ah9Z9/Z5jHA1Cwczr4NAAAAdAxBTQBRy2Q2K2P6VT7zZEy/UmarVdlXXxvUeXuGI+wTJ8tsbdnx3WQ2q89VnX3/5fGwS0J+gSw2WyfLQ9BF0JfMPdmSkmR2B9R8V9KWO0jWuLjQVyoCWOPiZMsdFMQSW//b2nIHKSYhoc1jgGdQk30bAAAA6DiCmgCiWvKYQmXPKmrWY9OQNS1d2bOKlDym0CufzIEf9lo8fm4yyT5pijKnTmu3fmol6NmReSbkFyinqPt/xCWamSLws9V58xbInJLiszehLXeQcufe33WVigC5c+8PWmCztbXu+Tdt6xhwbDr2bQAAACAwvFMTQNRLHlOopILR0iMfSZKSTsnXoCt+LFOzAGbymEIl/elZd76OsqSnS66m3ylnf095l4xvtYdm6/V72q/5Jpz2Xcnjey1Jp52u3Kum04srgkVoR023vHkLtPHjLdKKlu+RHLzwTz2mh2ZzuXPvV+Ira6SdZQFN3/cndylx5Ily1NdLj61wp7f2N23tGBA3aLCG/OZ29m0AAAAgQPTUBNAteAYwrXZ7i4Bma/k6Kin/+IdWkkeP8Sug2dH55tzwY6/hjCsIaEaLyOuneZw1Nrb19B4a0DymU8eDk06WyWxWTHy8V3pbf9Pm80oZM4Z9GwAAAOgEgpoAup1QBZeMMHTJM8IxU3QM6wgBMEV0GBwAAACIfAQ1AcBfHsGrrgpjES6LfO51RIwq6kTie1ABAAAA+IegJoDuJ0SBCpdHhLGrOufRCTB6EB4DAAAAgK5DUBNAtxO6x889I4xdE23k8fPIxyoCAAAAgK5HUBMA/BSO2JWLiFn04FHmqMMqAwAAAKIXQU0A3U+IAhWevSa7LNZITDMKsJIAAAAAoKsR1ATQ7XTF18+7Kozl6qL5oPPo9AcAAAAAXcca7goAQLQ4+tl/pJQhkqT9C/6ghJp9TSMsFsXk9Jezvk6u6mqpsvL4RFarZDZLDQ1S3ox257Hlxuu88u28//9UXl8uc0KCUiecp16TpshsjexDt8vh0OEP3lP58g+lo+WtZ7JYFJPdV70vvVxJJ50skzmy7rG5HA4dfOdtVb395vHEmBgljf2Osq66RhabzZ0czW8I2P/CXxSbkSlH6RHFZGTKPn6CDJdLB19+SdVrv/DelqWm57VjYxU3MFfpU76vxJEnRty6a87lcKj0Hx/o6MqVcu7fKzkc7nFVfc6RkgaEr3IAAAAAAhbZV8YAEABTqPrMebyAz/Cch9Opxt27Wp/GI4ASCKOhXnI45KqoUNkbr6vsjddlnzRFmVOndarcUCle8orKP3iv/YxOpxr3fqMDC/4gWSzK/vEsJY8pDH0F/dDmMjQ2quo//1bVf/6thPwC5RTNlnS81240vp+x6pOPVOUxfPjVl31PYBhSXZ3qNm/S/s2bZIqJUZ8bb46Yddec39sjAAAAgKgT2d0rACCChKNDntFKgLb8g/dUvOSVMNTGt4ADSE6nDjy5UJWrVwW/Uh3k7zLUrF2jvQsf64IaRTajsTFi1l1z/qxLE+9DBQAAAKIWPTUBdD8h6jG3IXmw+/frfce7f9+0e6ne6HOOsuqPqNoar10Jfd3j8qq+0bak/n7P4+Fmj6g/M/ASdzl96w8fH/G/vUrP3hG0R38tZpPi4mNUV9sop6vjgR7D5VLp//ZKaSdLkjYk5epwbJrPaa7Y/08NrtnvHi5++SUlFYwO2+PMLofDKwhWbYnT44OucA+ffeQLnVb2tXu4Zu0aORsaouI7QS6ns81xu+L76D/po3TZgY8U52qQSyYt6TtBOz224+a+W/qlrMbxN76a3vyf0ut6RUx31ebb4+bEAToU1yvMtQIAAAAQTAQ1AXQ7fXsndun8jgUeD8faW4zrSEDTl21J/VuW9emuoJQdNL0KOpT91b7nas62F9zDzvIy1W7ZrIThI4JdM7+UL//Qa9gzoClJH/ca7RXUlKSSVxdLaadKkkwREtBrTez61ZJaBvVcMmlxv4mSpKcHXKzbdy3RVylDfAY0Jemz9FNaJv57ZzCqGjwd3B67WmZafLirAAAAAEQ1gpoAuo37rhmjjbvLdNYp2T7zPXzzdzTnqf9KkgZmJWv3ocoWeXo1lKt/7SGtTT0hJHWVpNNLv1RaY6X+nnWGX/lPObrVa9jWt6/i8/KCUheTyaTY2BjV1zfKCODLN7Xbtqlhf1Ovy0azxatXa0c4jh4NaLpgaCwp7vg0xYdkpEV+V82hlbvUalDTIxBbY20Ksu2J7+NXmc23x/jhw2XLzAy4jsHkuT26TCZ9ldKx/eSumHVae/L5+vCLva2Ov/2yUV7DY0dk6n8bi/XY7b735TlXjdbDL32h+FirCob27lCdAAAAAHgjqAmg2xjSL1VD+qW2my8zLUHPzRnf5viaTRu199GmHoSnlX2tJ3MvC1odj/HsoXhS5Y4Wj523ZkrJCq/h3t/7odInBqdXo9VqVlpaosrKquVwuNqfoJnSZXt0+Mum+lVZ4gIOalpT219/oRKT0fGAXExmlvt35PbTlGKzsjTno6Zt7neDr5LTbJHU+jtb/dV8e8y59qyw9bJtznN7bDRZ/Apqeu6TKeeM11UTh+mqicP8mt/Mi0/SzIvbzzesv93nsQcAAACA//hQEAA0Ez/sBCk+wh8NNZlkHz8h3LVw86xLoGEyiz2t6W8fJoH8PTOumB4V79TMuGK6+7fnx3FaDWoG0FPXkhbedddcZ/cNz78XAAAAgMhEUBMAmjGZzcq+7oZwV8Mn+8TJMlsjp7O92WqVfdIUSZIpgKCYJGX+8KqwfSRI8l4GfyTkF8hisx1PiOCumhabTQn5Ld8x2eqaCuDdoJnTw7vumvPaHjsYdW6xXgEAAABEpMi5AgGACJI8plDZs4pC2gnPFBfXFHjpYDDIPmmKMqdOC1GtApc5dZrsk6Z0PLZnsSh7VpGSxxSGolodcmwZ2pOQX6CcotmSoqKjpiQpp2i2EvILvHtqdvLjRqaYmIhZd82512UHVpDnegUAAAAQ2SKnmw8ARJjkMYUaNHiE9NTnQS+77x0/VeKIkTKZzcq4bKr0yEftTpP2g8vUa9KUiOqh2Vzm1GmKn3KRtHBF+5klZd9+h5JOOjmievllTp2m3j+4THr0kxbjkk4/U1lXXePVk+9Yx1RTJHfV/FZO0WyZf/+x1OiUJCV+9wzp4PHxeX96VsnvbJQ2lrRbVt+f3KXEkSdG1LprLnPqNNkv+oH0h0/bzTvkiafpoQkAAABEkci9EgGASGAKzWEy6cST3MEgf4NCGd+/MKIDmsdYOlDH5FGnRGRQrK2/c98f3RD1gS/PzplZ0670Gme2Wv3uORxpwei2+LvPRPt6BQAAAHqayL8aAYAwipZHiyOJqZOPNEef6N1KjADffxpNetzmCAAAAPQQBDUBAEHVU4NI0bLcnvVsLaYZJYvht2h4LQAAAACAjiOoCQC+9ICebMHW04JI0baFeG7SrfXUjLblaVfP2hwBAACAHoOgJgD40O0CPF0gWnosBlu0LLbRxu/uKlrWCwAAAICOIagJAL70hKhPkPW4d2pG2zbi1VMzfNXoKj1uewQAAAB6CIKaAICg6s4xJJ+Pa0fhgveEDwUBAAAA6J6s4a5AV2hoaNCmTZsUGxurYcOG0WsDgN8I+XRcRw6xW268zmdBpqRk2c89T70mTZHZapXhcqly3Zc69P9ek3HwgORytZwsMVFJo8co84dXy3C59M0TC9W4ZZPkMmTp1Uu9Lr9Cdfv3qvIfy2TU1LQ9/7wZLet7049aPM5c2e8cKX5A1HR7NDy26u133yXlXuYe3nLjdarIOkNKHhyOqgEAAACA3yImqHn48GH9/Oc/14oVKzRo0CA9+OCDGj58eLvTbd68WRdddJFX2oQJE/TEE09IktatW6dZs2apV69eKi4uVr9+/fT8888rJSVFknTppZdq/fr1XtOvXLnSPR5Az0ZPto6r+mJ1cAoyDBmVFSp743WVvfG6EvILVLPuy1YDmV6TVVer8t+fqPLfn7QY5ywpVvGTCwOvkkwyNQt1Gw6nJKnklUWqtJyr5DGFAZcfapWrV8lV3yCZm5r/1rdubvwBAAAAiHwREdQ0DENFRUWSpNdee03r1q3TLbfcorfffluJiYk+p12zZo1OO+00LViwwJ1mtTYtltPp1J133qm77rpLl156qaqrq3XZZZdp0aJFmjlzpmpqarR582a9//776tWrl3v65OTkECwlAHR/latX6eCf/thqL8fOqlm7JuhlBpNJ0oEnF0qziiIysFm5elVT/QZf6ZHaMoBJGB8AAABANIiIoOYXX3yhNWvW6J133tGQIUM0ZMgQ/f3vf9c///lPXXzxxT6nXbNmjcaNG9dqz8rKykrNmDFDl156qSQpMTFRgwcP1tGjRyU19eLs16+fBg0aFPyFAoAexnC5VLL4pW7dz8+fgF/xyy8qqWC0TObIeW214XLpwHPPtEzndSwAAAAAolREBDU3bNigvn37Ki8vz51WUFCgL7/8st2g5hdffKGvv/5aTz/9tGJjYzVlyhTdc889iouLk91u14wZx3sLff7551qxYoVmzpzpnrampkZnnXWWysvLdcopp+jee+/VyJEjO7U8VmvkXMgGk8Vi9vof6AnMIdreAzlOhPLYEoz9u3rjZjnKyjo0zcPt9OhMbziqH+950yutNCZZTw/8gc/p7tn2gju4+odB01RviZUknVb6lc4uXaO3ss7QhgDeG/m7vGvazeMsL1fD9q1KHDGiw+WHSvXGzVJ9vaSmR+iPearZ37G99eGpu7V13W15PNF+A90X+zfQfbF/A+2LiKBmZWWlBgwY4JWWmpqqjRs3+pyuvLxcVVVVKioq0rnnnqtvvvlGP/3pTxUfH6+7777bK++FF16oLVu26Cc/+YlGjRolSdqxY4dGjx6t2267TXFxcZo/f75uvvlmLV++XDExMQEti9lsUlqa70fmo11KSny4qwB0mbiE2KCXmWLUBXSc6IpjS2f2b4ejNog1aVJqS1WdOUZxrkZ32ubEge1OVx6TrLTGSklyBzQlaUX6yTq7dE1AAc2OiHXURlRb4LluJh7+XO9lfrdT5V32vbyIWr5g6G7L0xrab6D7Yv8Gui/2b6BtERHUtFqtio31DhzExcWpxtdXaSXZ7XatWLHCPZyVlaWioiLNnz+/RVDz2Wef1bvvvqt58+YpPz9fp512mh599FGvPA888IDGjRun//73vzrzzDMDWhaXy1BFhe96RyuLxayUlHhVVNTK6fT9oQ6gO5l/+xl6+q0NcrpcitmzTQllhyRJ/0s70a/ph1XtVqKzTmbDpVMqtipn5BCVlVV75Xnq/ztHH6/dp0OltSqrrNfVk05QTV2j/vXFPlnMJl16dstpgikY+3e99fgJ1wUH/62/92k6jo4u3yTDZFKcs16V1kTVmW3altTf73KNZg+0u/x4ZNqpIN7RNgy/Punu+QGhemt8SNdXR3mum1Mqtvkd1PxO2deyuRq0PSFHg6zVijvtTH33pGwN7pcSUcvXnvm3n6Evtx3W4aN12ltcJZchJcU3nQL1y0jSeYX9o2p5Oor2G+i+2L+B7ov9Gz1ZSkq8X72UIyKomZaWpsOHD3ulVVVVyWazdbiszMxMlZSUqL6+3itQmpWVpR/96EfaunWrXn75ZZ122mktpo2Pj1dycrL27t3b8YXw4HB07wOO0+nq9ssIeEpJsOmn0/MlSY01w7Xz9lskSU6TWavt7T9i/N2yr9SnvtQ9nHPjTS32oRiLWeeO8Q702RNtuuq8Ye7hrtjvOrN/24YMlTUtTY6yMqU6jgeIzj28UuZmb6PsyGPOzYOazYdDbfr+f2hxv4l+57fY7bINGRpRx0nbkKFSbKz7EXR/9Kst1jlHvpAkfbfsa+XOXyhbUpIkyeU05IqiTwqlJNh05qi+PvNE0voKFdpvoPti/wa6L/ZvoG0R8XKG/Px8bd26VRUVFe60r776StnZ2T6n+/jjj3Xrrbd6pa1evVq9evVSbGys1q9fr9tuu02GcfzCy2azyWKxyOFw6IILLtD+/fvd43bv3q3Dhw+rb1/fFz4Aeq6YhARZMzIDnt6WO0jWuLgg1ihymMxmZUy/qmV6FAW/giHzh1dH1EeCpKZ1k339TR2bxuO3OSXFHdAEAAAAgEgQEVddQ4cO1eDBgzVv3jy5XC6tX79ey5Yt0/jx4+VyuVRRUSGn09liuhNPPFErVqzQ008/rU2bNumll17S888/r2uuafqQw6BBg7RmzRr94he/0L59+/TJJ5/o7bff1oUXXiir1aq8vDzde++9Wrt2rVasWKHZs2dryJAhOv3007v6TwAgigx+6JGAApu23EHKnXt/CGoUOZLHFCp7VpHMycle6da0dGXPKpItd1CHyzz2he6E/ALJbI7YL3abDCl7VpGSxxSGuyqtOrZu/NcUjDanpChv3oLQVAoAAAAAAmQyPLsxhtGGDRt08803q7GxUZWVlbr44ov14IMPau/evZowYYKWLl2qEa18SfZ///ufHnjgAe3YsUP9+vXTDTfcoKlTp8r07UXvpk2b9Jvf/EZff/21srKyNHPmTF1yySWSpIqKCt1///366KOPZLPZdO655+quu+5Senp6wMvhdLpUWto938tltZqVlpaosrJqur8Dkv723gb968uD7eabGbdVo2deG9E9NIO9f2/aXapHXl4rSVp4Sbbih53g7r3oqKvTj+d/5ndZ9w2v0aALJststcpwufTK6//Vsu11PqeZ3XufTpoxTYbL1WJe885L0Z3/qGhjypZ+PMqmp9c1tJvvqvOGasIY/98VGi6Gy6UbHvmo3XwDHaW69yeT6aHZDdB+A90X+zfQfbF/oydLT0+MnndqStLIkSP1wQcfaOXKlUpLS3N/oTwnJ0ebN29uc7qxY8fqrbfeanP88OHD9be//a3VcSkpKfrDH/7QuYoD6LHMFv8OoVnTr4zogGYomDx6UyYM974h1dG/RfqE82S2Nv2tTWazbFlZ0vbdPqfJuORSWdp4L7N9TKH0j+V+zz955Ehp3dp285kjtAdpc/4+Gp8wZAgBTQAAAAARK2KCmpKUkJCgs88+O9zVAAB0UjCfAWj+QEGXP18QEc8zdL0oidECAAAA6KEi4p2aABCVCPp0ieZBTJcfUc1gxiENf0vrZlHAaOl5CgAAAKBnIqgJAIHqoT34/BHUoGIgPTUj43XR0Y2YJgAAAIAIRlATABDRWoQn/YhXBjWk6W9HzWDOMwKYut0SAQAAAOhOCGoCQKD8jPn0yKd4g9hTsnlPTb8ePw/mOz39zdjN1nOP3G4BAAAARA2CmgAQKJ5wblNwHz/3Pdz6NMEMqgatKAAAAABAkBDUBIAA+f0BmR4ouB/qaT7ctT01/V2a7taxsbstDwAAAIDuxRruCgBAtOKdgz4EMai48745qmiscA+X9T5Vso/wOc3uX9+v+vrSpoG8GV7jttx4XYs0X/wNkB559+9qHHaNYhIS/C47kpnNbN8AAAAAIhc9NQEgQP721DT1wJcTBrMXq9EseNx82J9puoLzcIl23n6Ldtx7d5fPGwAAAAB6GoKaABAgHj7vGkazoHDz4VanCWJQs6Pr2VFSTGATAAAAAEKMx88BAMEXxIjvnvgsOU1m1Zpj5TBZtDcus91p9sVlSDLkMrW8d7c7PqtjFfB3WTzyOUqK1VhTE9WPopt7YA9jAAAAANGDoCYABMhm9a+ze098NWEw38f4j4xxHZ7mw4xT2xz3cr9JHSrL32WptHoHMPc/Nk8D753boXlFElsMD3MAAAAAiFxcsQBAgL5/Wm6r6SkJMfr/pue7h/v2TuyaCkWQ4QPSNHyAXRPG5IS7Kp124qA0JSfEtJtvY3Ku17CjtDRENeq8qycOazfPVee1nwcAAAAAwsVkGP5+1xX+cDpdKi2tDnc1QsJqNSstLVFlZdVyOFz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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0      1      2\n",
      "0     买涨  0.550      5\n",
      "1     买涨  0.550     10\n",
      "2     买涨  0.550     13\n",
      "3     买涨  0.551     30\n",
      "4     买涨  0.551     38\n",
      "...   ..    ...    ...\n",
      "3868  买涨  0.543  45426\n",
      "3869  买涨  0.543  45431\n",
      "3870  买涨  0.543  45441\n",
      "3871  买涨  0.543  45449\n",
      "3872  买涨  0.543  45454\n",
      "\n",
      "[3873 rows x 3 columns]\n",
      "513130利润率: -0.06732252305358187\n",
      "513130收益率 1.000000069204169\n",
      "513130胜率: 0.39534282018111255\n",
      "       代码   夏普率      Beta       收益率\n",
      "0  513130  None  1.000022 -0.067323\n"
     ]
    }
   ],
   "source": [
    "import efinance as ef\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import re\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "# data_kua = pd.read_excel(r\"C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\排名代码表.xlsx\")\n",
    "# code_names = data_kua['thscode']\n",
    "code_data = []\n",
    "shouyilvlist = []\n",
    "# print(code_names)\n",
    "# for i in code_names:\n",
    "#     if i not in code_data:\n",
    "#         code_data.append(i)\n",
    "# print(code_data)\n",
    "\n",
    "db_all_ = []\n",
    "\n",
    "neam_ids = ef.futures.get_futures_base_info()\n",
    "hua = 0.01\n",
    "def get_name_index(name):\n",
    "    return neam_ids['行情ID'][neam_ids[neam_ids['期货名称'] == f'{name}'].index.tolist()[0]]\n",
    "\n",
    "def split_to_data(data,name,wheel):\n",
    "    data_wheel = []\n",
    "    for i in range(5,len(data)):\n",
    "        data_wheel.append(np.average(data[name][i-wheel:i-1]))\n",
    "    return data_wheel\n",
    "def find_h_l(data,wheel):\n",
    "    data_h = []\n",
    "    data_l = []\n",
    "    close_h = []\n",
    "    close_l = []\n",
    "    for i in range(5, len(data)):\n",
    "        data_h.append(np.max(data['high'][i - wheel:i - 1]))\n",
    "        data_l.append(np.min(data['low'][i - wheel:i - 1]))\n",
    "        close_h.append(np.max(data['close'][i - wheel:i - 1]))\n",
    "        close_l.append(np.min(data['close'][i - wheel:i - 1]))\n",
    "    return data_h,data_l,close_h,close_l\n",
    "\n",
    "def one_to(data_to_one):\n",
    "    one_to_data = []\n",
    "    pif = np.max(data_to_one) - np.min(data_to_one)\n",
    "    for i in data_to_one:\n",
    "        one_to_data.append((i - np.min(data_to_one))/pif)\n",
    "    return one_to_data\n",
    "\n",
    "def get_have(name:str)->str:\n",
    "    name = re.findall(\"\\d+\",name)[0]\n",
    "    return name\n",
    "\n",
    "def wlh(address,grid):\n",
    "    if address[0] != grid:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "def shouyi(data_shou):\n",
    "    pass\n",
    "def get_shouyilv(data_do:pd.DataFrame):\n",
    "    data = np.array(data_do[1])\n",
    "    index_list = data_do[0][data_do[0]=='卖出'].index.tolist()\n",
    "    index_list.insert(0,0)\n",
    "    sum_ = 0\n",
    "    win_lv = 0\n",
    "    for i in range(len(index_list)-1):\n",
    "        end = data[index_list[i+1]]\n",
    "        k = 1\n",
    "        if i == 0:\n",
    "            k = 0\n",
    "        for j in range(index_list[i]+k,index_list[i+1]):\n",
    "            diff = end*0.99 - data[j] - end*0.0005\n",
    "            sum_ += (diff)/data[j]\n",
    "            if diff > 0:\n",
    "                win_lv += 1\n",
    "    return sum_,(win_lv/(len(data_do)-len(index_list)+1))\n",
    "def main(id_futures):\n",
    "    \"\"\"\n",
    "    k1 = 0.5\n",
    "    k2 = 0.1\n",
    "    \"\"\"\n",
    "    cc = []\n",
    "    k1 = 0.\n",
    "    k2 = 0.1\n",
    "    wheel = 5\n",
    "    guo = 0.02\n",
    "    ####网格交易参数\n",
    "    data_do = [[-3]]\n",
    "    address = [0,0]\n",
    "    get_buy_data = 0\n",
    "    last_ = 0\n",
    "    # max_volume = 15\n",
    "    # print(qs.latest_trade_date())\n",
    "    # data_base = qs.get_data(id_futures, fqt=0,freq=5,start='2023-11-09',end='2023-11-17')\n",
    "    # try:\n",
    "    #     data_base.to_excel(r'20231110-23231116每分钟数据.xlsx')\n",
    "    # except:\n",
    "    #     print(\"已保存\")\n",
    "    # print(data_base)\n",
    "    # if len(data_base) == 0:\n",
    "    #     return\n",
    "    # data_base_open = np.array(data_base['open'])\n",
    "    ggb = 0\n",
    "    data_base_close = np.array(all_data_to_gf['latest'])\n",
    "    benefit = []\n",
    "    for i in range(len(data_base_close)-1):\n",
    "        benefit.append(data_base_close[i+1]/data_base_close[i])\n",
    "    jzsy = np.sum(benefit)/(len(data_base_close)-1)\n",
    "    beta = np.cov(benefit)/np.var(benefit)\n",
    "    # data_h, data_l, close_h, close_l = find_h_l(data_base,wheel)\n",
    "    \n",
    "    for i in range(5,len(data_base_close)):\n",
    "        before = i-1\n",
    "        center = data_base_close[before]\n",
    "        wg = np.array([0.95, 0.96, 0.97, 0.98, 1, 1.01, 1.02, 1.03, 1.04]) * center\n",
    "        grid = pd.cut([data_base_close[i]], wg, labels=[1, 2, 3, 4, 5, 6, 7, 8])[0]\n",
    "        \"\"\"# range_k = max(data_h[i-5]-close_l[i-5],close_h[i-5]-data_l[i-5])\n",
    "        # buy_line = data_base_open[i] + k1*range_k\n",
    "        # sell_line = data_base_open[i] - k2*range_k\n",
    "        网格\"\"\"\n",
    "        # if get_buy_data >= 150:\n",
    "        #     data_do.append(['卖出', data_base_close[i], i])\n",
    "        #     get_buy_data = 0\n",
    "        if last_ > grid and wlh(address,grid):\n",
    "            data_do.append(['卖出', data_base_close[i], i])\n",
    "        if last_ < grid and wlh(address,grid):\n",
    "            data_do.append(['买涨', data_base_close[i], i])\n",
    "            get_buy_data += 1\n",
    "        address = [last_, grid]\n",
    "        last_ = grid\n",
    "        if i == len(data_base_close)+4:\n",
    "            data_do.append(['卖出', data_base_close[i], i])\n",
    "            get_buy_data = 0\n",
    "    data_do = data_do[1:]\n",
    "    \n",
    "    a = []\n",
    "    b = []\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.plot(range(len(data_base_close)),data_base_close)\n",
    "    for d_dat in data_do:\n",
    "        color = str\n",
    "        if d_dat[0] != '买涨':\n",
    "            color = 'g'\n",
    "            a = plt.scatter(d_dat[2], d_dat[1], c=color,s = 200)\n",
    "        else:\n",
    "            color = 'r'\n",
    "            b = plt.scatter(d_dat[2],d_dat[1],c = color)\n",
    "\n",
    "    plt.legend((a, b), ('卖出', '买入'), loc='best')\n",
    "    plt.xlabel('时间')\n",
    "    plt.ylabel('收盘价')\n",
    "    plt.show()\n",
    "    data_do = pd.DataFrame(data_do)\n",
    "    print(data_do)\n",
    "    data_do.to_excel(r'操作明细表.xlsx')\n",
    "    shouyilv,win_lv = get_shouyilv(data_do)\n",
    "    try:\n",
    "        xpl = (shouyilv - guo)/bodonglv\n",
    "    except:\n",
    "        xpl = None\n",
    "    print(f'{id_futures}利润率:',shouyilv)\n",
    "    print(f'{id_futures}收益率',jzsy)\n",
    "    print(f'{id_futures}胜率:',win_lv)\n",
    "    try:\n",
    "        bodonglv = math.std(shouyilv)/shouyilv\n",
    "    except:\n",
    "        bodonglv = None\n",
    "    shouyilvlist.append([id_futures,xpl,beta,shouyilv])\n",
    "\n",
    "\n",
    "for index in range(1):\n",
    "    id_futures = str('513130')\n",
    "    main(id_futures)\n",
    "    end_data = pd.DataFrame(shouyilvlist, columns=['代码', '夏普率', 'Beta', '收益率'])\n",
    "    print(end_data)\n",
    "    # end_data.to_excel('收益率(网格交易法+Dual_Thrust).xlsx', index_label=None)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.55  0.55  0.551 ... 0.543 0.543 0.543] [0.55  0.551 0.55  ... 0.543 0.543 0.543]\n",
      "Epoch 1/5\n",
      "45416/45416 - 45s - loss: 1.8856e-04 - 45s/epoch - 999us/step\n",
      "Epoch 2/5\n",
      "45416/45416 - 51s - loss: 1.3450e-06 - 51s/epoch - 1ms/step\n",
      "Epoch 3/5\n",
      "45416/45416 - 49s - loss: 1.2264e-06 - 49s/epoch - 1ms/step\n",
      "Epoch 4/5\n",
      "45416/45416 - 53s - loss: 1.1278e-06 - 53s/epoch - 1ms/step\n",
      "Epoch 5/5\n",
      "45416/45416 - 53s - loss: 1.0231e-06 - 53s/epoch - 1ms/step\n",
      "1/1 [==============================] - 0s 125ms/step\n",
      "预测结果: [0.542      0.543      0.543      0.543      0.542      0.542\n",
      " 0.543      0.543      0.543      0.543      0.542      0.543\n",
      " 0.542      0.543      0.543      0.543      0.543      0.543\n",
      " 0.543      0.543      0.542      0.543      0.542      0.543\n",
      " 0.543      0.543      0.543      0.543      0.542      0.543\n",
      " 0.542      0.543      0.543      0.542      0.543      0.543\n",
      " 0.543      0.543      0.543      0.543      0.54381329]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "import numpy as np\n",
    "import efinance as ef\n",
    "# 示例数据\n",
    "x = np.array(all_data_to_gf['latest'])[:-40]\n",
    "y = x[1:]\n",
    "x = x[:-1]\n",
    "print(x,y)\n",
    "\n",
    "# 将数据转换为 LSTM 模型的输入格式\n",
    "def create_lstm_dataset(data, look_back=1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data) - look_back):\n",
    "        X.append(data[i:(i + look_back)])\n",
    "        y.append(data[i + look_back])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "look_back = 3\n",
    "X, y = create_lstm_dataset(x, look_back)\n",
    "\n",
    "# 转换为 LSTM 模型需要的输入格式 (samples, time steps, features)\n",
    "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
    "\n",
    "# 定义 LSTM 模型\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, activation='relu', input_shape=(look_back, 1)))\n",
    "model.add(Dense(units=1))  # 输出层\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X, y, epochs=5, batch_size=1, verbose=2)\n",
    "\n",
    "# 预测未来的多个时间步\n",
    "future_data = np.array(all_data_to_gf['latest'])[-40:]\n",
    "\n",
    "# 单步预测\n",
    "predicted_value = model.predict(np.reshape(future_data[-look_back:], (1, look_back, 1)))\n",
    "\n",
    "# 将预测值添加到 future_data 中\n",
    "future_data = np.append(future_data, predicted_value)\n",
    "\n",
    "print(\"预测结果:\", future_data)\n",
    "\n",
    "# 绘制真实值和预测值\n",
    "plt.plot(range(len(np.array(all_data_to_gf['latest'])[-40:])), np.array(all_data_to_gf['latest'])[-40:])\n",
    "plt.plot(range(len(future_data)), future_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "fig=plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.plot(range(len(np.array(all_data_to_gf['latest'])[-40:])), np.array(all_data_to_gf['latest'])[-40:],label = \"Ture\")\n",
    "ax1.set_title('实际值')\n",
    "# plt.legend()\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.plot(range(len(future_data)), future_data,label = \"Predice\")\n",
    "ax2.set_title('预测值')\n",
    "# plt.legend()\n",
    "plt.subplots_adjust(top=0.9, bottom=0.1, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>代码</th>\n",
       "      <th>日期</th>\n",
       "      <th>主力净流入</th>\n",
       "      <th>小单净流入</th>\n",
       "      <th>中单净流入</th>\n",
       "      <th>大单净流入</th>\n",
       "      <th>超大单净流入</th>\n",
       "      <th>主力净流入占比</th>\n",
       "      <th>小单流入净占比</th>\n",
       "      <th>中单流入净占比</th>\n",
       "      <th>大单流入净占比</th>\n",
       "      <th>超大单流入净占比</th>\n",
       "      <th>收盘价</th>\n",
       "      <th>涨跌幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>恒生科技ETF</td>\n",
       "      <td>513130</td>\n",
       "      <td>2023-11-10</td>\n",
       "      <td>-107008736.0</td>\n",
       "      <td>33140833.0</td>\n",
       "      <td>73867897.0</td>\n",
       "      <td>83535456.0</td>\n",
       "      <td>-190544192.0</td>\n",
       "      <td>-6.69</td>\n",
       "      <td>2.07</td>\n",
       "      <td>4.62</td>\n",
       "      <td>5.22</td>\n",
       "      <td>-11.91</td>\n",
       "      <td>0.534</td>\n",
       "      <td>-2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>恒生科技ETF</td>\n",
       "      <td>513130</td>\n",
       "      <td>2023-11-13</td>\n",
       "      <td>-333044.0</td>\n",
       "      <td>2203713.0</td>\n",
       "      <td>-1870664.0</td>\n",
       "      <td>-44231300.0</td>\n",
       "      <td>43898256.0</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>恒生科技ETF</td>\n",
       "      <td>513130</td>\n",
       "      <td>2023-11-14</td>\n",
       "      <td>-23769046.0</td>\n",
       "      <td>4315941.0</td>\n",
       "      <td>19453102.0</td>\n",
       "      <td>25718970.0</td>\n",
       "      <td>-49488016.0</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.88</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>恒生科技ETF</td>\n",
       "      <td>513130</td>\n",
       "      <td>2023-11-15</td>\n",
       "      <td>22113600.0</td>\n",
       "      <td>-15669174.0</td>\n",
       "      <td>-6444410.0</td>\n",
       "      <td>-29464416.0</td>\n",
       "      <td>51578016.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.557</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>恒生科技ETF</td>\n",
       "      <td>513130</td>\n",
       "      <td>2023-11-16</td>\n",
       "      <td>-43350064.0</td>\n",
       "      <td>12024277.0</td>\n",
       "      <td>31325780.0</td>\n",
       "      <td>31345488.0</td>\n",
       "      <td>-74695552.0</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.41</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>0.553</td>\n",
       "      <td>-0.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          名称      代码          日期        主力净流入       小单净流入       中单净流入  \\\n",
       "96   恒生科技ETF  513130  2023-11-10 -107008736.0  33140833.0  73867897.0   \n",
       "97   恒生科技ETF  513130  2023-11-13    -333044.0   2203713.0  -1870664.0   \n",
       "98   恒生科技ETF  513130  2023-11-14  -23769046.0   4315941.0  19453102.0   \n",
       "99   恒生科技ETF  513130  2023-11-15   22113600.0 -15669174.0  -6444410.0   \n",
       "100  恒生科技ETF  513130  2023-11-16  -43350064.0  12024277.0  31325780.0   \n",
       "\n",
       "          大单净流入       超大单净流入  主力净流入占比  小单流入净占比  中单流入净占比  大单流入净占比  超大单流入净占比  \\\n",
       "96   83535456.0 -190544192.0    -6.69     2.07     4.62     5.22    -11.91   \n",
       "97  -44231300.0   43898256.0    -0.03     0.17    -0.15    -3.43      3.40   \n",
       "98   25718970.0  -49488016.0    -1.74     0.32     1.42     1.88     -3.62   \n",
       "99  -29464416.0   51578016.0     1.08    -0.77    -0.32    -1.44      2.53   \n",
       "100  31345488.0  -74695552.0    -1.96     0.54     1.41     1.41     -3.37   \n",
       "\n",
       "       收盘价   涨跌幅  \n",
       "96   0.534 -2.91  \n",
       "97   0.538  0.75  \n",
       "98   0.543  0.93  \n",
       "99   0.557  2.58  \n",
       "100  0.553 -0.72  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=qs.hist_money('513130')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.76s/it]\n"
     ]
    },
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        },
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         "yanchor": "top"
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        "xaxis": {
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   "source": [
    "#1:'percent',2:'probability',3:'density',4:'probability density'\n",
    "#1：'百分比'，2：'概率'，3：'密度'，4：'概率密度'\n",
    "code='513130'\n",
    "data=qs.get_data(code,fqt=1)\n",
    "plot.hist(all_data_to_gf,x='latest',histnorm=4,title=code+'股价直方图')"
   ]
  },
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   "execution_count": null,
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    "plot.box(df['close'])"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "source": [
    "df1=qs.eps_forecast()"
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  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>研报数</th>\n",
       "      <th>买入</th>\n",
       "      <th>增持</th>\n",
       "      <th>中性</th>\n",
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       "      <th>2023每股收益</th>\n",
       "      <th>2024每股收益</th>\n",
       "      <th>2025每股收益</th>\n",
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       "      <th>22</th>\n",
       "      <td>603288</td>\n",
       "      <td>海天味业</td>\n",
       "      <td>38</td>\n",
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       "      <td>15.0</td>\n",
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      "text/plain": [
       "        代码    名称  研报数    买入    增持  中性  减持  卖出  2022每股收益  2023每股收益  2024每股收益  \\\n",
       "22  603288  海天味业   38  23.0  15.0 NaN NaN NaN  1.114577  1.097526  1.256316   \n",
       "\n",
       "    2025每股收益  \n",
       "22  1.424216  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
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    }
   ],
   "source": [
    "df1.loc[df1[df1['代码'] == '603288'].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "xgx = pd.read_excel(r'C:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\附录\\回测数据\\第三题\\相关性表.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "xggp = np.array(xgx.loc[xgx[xgx[0] == \"['513130.SH']\"].index].iloc[:,2:].dropna(axis=1),dtype=int)[0].tolist()"
   ]
  },
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      "100%|██████████| 1/1 [00:01<00:00,  1.22s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "100%|██████████| 1/1 [00:01<00:00,  1.26s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.16s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "100%|██████████| 1/1 [00:01<00:00,  1.22s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "100%|██████████| 1/1 [00:01<00:00,  1.19s/it]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n"
     ]
    }
   ],
   "source": [
    "all_df = pd.DataFrame()\n",
    "for code_id in xggp:\n",
    "    xg_data = qs.get_data(f'{code_id}',start='20230101')['close']\n",
    "    all_df = pd.concat([all_df,xg_data],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2023-11-09 09:30:00    0.550\n",
       "2023-11-09 09:31:00    0.550\n",
       "2023-11-09 09:32:00    0.551\n",
       "2023-11-09 09:33:00    0.552\n",
       "2023-11-09 09:34:00    0.552\n",
       "                       ...  \n",
       "2023-11-15 14:56:00    0.557\n",
       "2023-11-15 14:57:00    0.557\n",
       "2023-11-15 14:58:00    0.557\n",
       "2023-11-15 14:59:00    0.558\n",
       "2023-11-15 15:00:00    0.557\n",
       "Name: close, Length: 1205, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df.columns = xggp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>603288</th>\n",
       "      <th>600887</th>\n",
       "      <th>603799</th>\n",
       "      <th>600887</th>\n",
       "      <th>300015</th>\n",
       "      <th>603288</th>\n",
       "      <th>603799</th>\n",
       "      <th>600009</th>\n",
       "      <th>300999</th>\n",
       "      <th>600763</th>\n",
       "      <th>...</th>\n",
       "      <th>600882</th>\n",
       "      <th>2741</th>\n",
       "      <th>688</th>\n",
       "      <th>2759</th>\n",
       "      <th>2461</th>\n",
       "      <th>603920</th>\n",
       "      <th>601700</th>\n",
       "      <th>2378</th>\n",
       "      <th>2088</th>\n",
       "      <th>300755</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>64.16</td>\n",
       "      <td>29.66</td>\n",
       "      <td>56.00</td>\n",
       "      <td>29.66</td>\n",
       "      <td>23.38</td>\n",
       "      <td>64.16</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.25</td>\n",
       "      <td>42.98</td>\n",
       "      <td>146.98</td>\n",
       "      <td>...</td>\n",
       "      <td>31.18</td>\n",
       "      <td>18.51</td>\n",
       "      <td>3.14</td>\n",
       "      <td>16.28</td>\n",
       "      <td>7.72</td>\n",
       "      <td>14.18</td>\n",
       "      <td>5.18</td>\n",
       "      <td>5.45</td>\n",
       "      <td>22.09</td>\n",
       "      <td>30.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>63.58</td>\n",
       "      <td>29.54</td>\n",
       "      <td>55.41</td>\n",
       "      <td>29.54</td>\n",
       "      <td>23.53</td>\n",
       "      <td>63.58</td>\n",
       "      <td>55.41</td>\n",
       "      <td>56.43</td>\n",
       "      <td>42.81</td>\n",
       "      <td>147.20</td>\n",
       "      <td>...</td>\n",
       "      <td>31.25</td>\n",
       "      <td>18.52</td>\n",
       "      <td>3.15</td>\n",
       "      <td>16.09</td>\n",
       "      <td>7.73</td>\n",
       "      <td>13.99</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.38</td>\n",
       "      <td>21.49</td>\n",
       "      <td>29.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>66.05</td>\n",
       "      <td>30.50</td>\n",
       "      <td>58.78</td>\n",
       "      <td>30.50</td>\n",
       "      <td>23.99</td>\n",
       "      <td>66.05</td>\n",
       "      <td>58.78</td>\n",
       "      <td>58.34</td>\n",
       "      <td>43.75</td>\n",
       "      <td>151.05</td>\n",
       "      <td>...</td>\n",
       "      <td>33.09</td>\n",
       "      <td>18.58</td>\n",
       "      <td>3.16</td>\n",
       "      <td>16.48</td>\n",
       "      <td>7.93</td>\n",
       "      <td>14.10</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.38</td>\n",
       "      <td>22.23</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-06</th>\n",
       "      <td>65.50</td>\n",
       "      <td>30.46</td>\n",
       "      <td>59.56</td>\n",
       "      <td>30.46</td>\n",
       "      <td>23.61</td>\n",
       "      <td>65.50</td>\n",
       "      <td>59.56</td>\n",
       "      <td>57.00</td>\n",
       "      <td>43.80</td>\n",
       "      <td>149.24</td>\n",
       "      <td>...</td>\n",
       "      <td>32.87</td>\n",
       "      <td>18.75</td>\n",
       "      <td>3.15</td>\n",
       "      <td>16.66</td>\n",
       "      <td>7.71</td>\n",
       "      <td>14.25</td>\n",
       "      <td>5.54</td>\n",
       "      <td>5.40</td>\n",
       "      <td>22.17</td>\n",
       "      <td>30.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09</th>\n",
       "      <td>66.27</td>\n",
       "      <td>30.56</td>\n",
       "      <td>59.36</td>\n",
       "      <td>30.56</td>\n",
       "      <td>23.61</td>\n",
       "      <td>66.27</td>\n",
       "      <td>59.36</td>\n",
       "      <td>58.53</td>\n",
       "      <td>44.69</td>\n",
       "      <td>150.60</td>\n",
       "      <td>...</td>\n",
       "      <td>33.92</td>\n",
       "      <td>18.58</td>\n",
       "      <td>3.15</td>\n",
       "      <td>16.70</td>\n",
       "      <td>7.83</td>\n",
       "      <td>14.33</td>\n",
       "      <td>5.43</td>\n",
       "      <td>5.51</td>\n",
       "      <td>22.46</td>\n",
       "      <td>31.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-09</th>\n",
       "      <td>38.51</td>\n",
       "      <td>27.63</td>\n",
       "      <td>37.21</td>\n",
       "      <td>27.63</td>\n",
       "      <td>18.03</td>\n",
       "      <td>38.51</td>\n",
       "      <td>37.21</td>\n",
       "      <td>37.21</td>\n",
       "      <td>34.83</td>\n",
       "      <td>90.51</td>\n",
       "      <td>...</td>\n",
       "      <td>17.46</td>\n",
       "      <td>14.54</td>\n",
       "      <td>2.97</td>\n",
       "      <td>11.88</td>\n",
       "      <td>8.18</td>\n",
       "      <td>17.86</td>\n",
       "      <td>4.90</td>\n",
       "      <td>5.66</td>\n",
       "      <td>15.53</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-10</th>\n",
       "      <td>38.36</td>\n",
       "      <td>27.52</td>\n",
       "      <td>36.81</td>\n",
       "      <td>27.52</td>\n",
       "      <td>17.71</td>\n",
       "      <td>38.36</td>\n",
       "      <td>36.81</td>\n",
       "      <td>37.09</td>\n",
       "      <td>34.71</td>\n",
       "      <td>89.38</td>\n",
       "      <td>...</td>\n",
       "      <td>17.31</td>\n",
       "      <td>14.60</td>\n",
       "      <td>2.98</td>\n",
       "      <td>11.83</td>\n",
       "      <td>8.05</td>\n",
       "      <td>17.86</td>\n",
       "      <td>4.94</td>\n",
       "      <td>5.63</td>\n",
       "      <td>15.50</td>\n",
       "      <td>19.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-13</th>\n",
       "      <td>38.25</td>\n",
       "      <td>27.43</td>\n",
       "      <td>37.08</td>\n",
       "      <td>27.43</td>\n",
       "      <td>17.57</td>\n",
       "      <td>38.25</td>\n",
       "      <td>37.08</td>\n",
       "      <td>37.19</td>\n",
       "      <td>34.55</td>\n",
       "      <td>88.25</td>\n",
       "      <td>...</td>\n",
       "      <td>17.45</td>\n",
       "      <td>14.76</td>\n",
       "      <td>3.03</td>\n",
       "      <td>11.87</td>\n",
       "      <td>8.07</td>\n",
       "      <td>18.03</td>\n",
       "      <td>4.97</td>\n",
       "      <td>5.72</td>\n",
       "      <td>15.41</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-14</th>\n",
       "      <td>38.35</td>\n",
       "      <td>27.27</td>\n",
       "      <td>36.69</td>\n",
       "      <td>27.27</td>\n",
       "      <td>17.64</td>\n",
       "      <td>38.35</td>\n",
       "      <td>36.69</td>\n",
       "      <td>37.25</td>\n",
       "      <td>34.69</td>\n",
       "      <td>88.40</td>\n",
       "      <td>...</td>\n",
       "      <td>17.62</td>\n",
       "      <td>14.92</td>\n",
       "      <td>3.00</td>\n",
       "      <td>11.84</td>\n",
       "      <td>8.08</td>\n",
       "      <td>18.33</td>\n",
       "      <td>5.03</td>\n",
       "      <td>5.71</td>\n",
       "      <td>15.42</td>\n",
       "      <td>19.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-15</th>\n",
       "      <td>39.15</td>\n",
       "      <td>27.26</td>\n",
       "      <td>37.20</td>\n",
       "      <td>27.26</td>\n",
       "      <td>17.63</td>\n",
       "      <td>39.15</td>\n",
       "      <td>37.20</td>\n",
       "      <td>37.36</td>\n",
       "      <td>35.24</td>\n",
       "      <td>89.01</td>\n",
       "      <td>...</td>\n",
       "      <td>17.76</td>\n",
       "      <td>15.53</td>\n",
       "      <td>3.01</td>\n",
       "      <td>11.98</td>\n",
       "      <td>8.14</td>\n",
       "      <td>18.50</td>\n",
       "      <td>5.03</td>\n",
       "      <td>5.80</td>\n",
       "      <td>15.55</td>\n",
       "      <td>19.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            603288  600887  603799  600887  300015  603288  603799  600009  \\\n",
       "2023-01-03   64.16   29.66   56.00   29.66   23.38   64.16   56.00   56.25   \n",
       "2023-01-04   63.58   29.54   55.41   29.54   23.53   63.58   55.41   56.43   \n",
       "2023-01-05   66.05   30.50   58.78   30.50   23.99   66.05   58.78   58.34   \n",
       "2023-01-06   65.50   30.46   59.56   30.46   23.61   65.50   59.56   57.00   \n",
       "2023-01-09   66.27   30.56   59.36   30.56   23.61   66.27   59.36   58.53   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2023-11-09   38.51   27.63   37.21   27.63   18.03   38.51   37.21   37.21   \n",
       "2023-11-10   38.36   27.52   36.81   27.52   17.71   38.36   36.81   37.09   \n",
       "2023-11-13   38.25   27.43   37.08   27.43   17.57   38.25   37.08   37.19   \n",
       "2023-11-14   38.35   27.27   36.69   27.27   17.64   38.35   36.69   37.25   \n",
       "2023-11-15   39.15   27.26   37.20   27.26   17.63   39.15   37.20   37.36   \n",
       "\n",
       "            300999  600763  ...  600882  2741    688     2759    2461    \\\n",
       "2023-01-03   42.98  146.98  ...   31.18   18.51    3.14   16.28    7.72   \n",
       "2023-01-04   42.81  147.20  ...   31.25   18.52    3.15   16.09    7.73   \n",
       "2023-01-05   43.75  151.05  ...   33.09   18.58    3.16   16.48    7.93   \n",
       "2023-01-06   43.80  149.24  ...   32.87   18.75    3.15   16.66    7.71   \n",
       "2023-01-09   44.69  150.60  ...   33.92   18.58    3.15   16.70    7.83   \n",
       "...            ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "2023-11-09   34.83   90.51  ...   17.46   14.54    2.97   11.88    8.18   \n",
       "2023-11-10   34.71   89.38  ...   17.31   14.60    2.98   11.83    8.05   \n",
       "2023-11-13   34.55   88.25  ...   17.45   14.76    3.03   11.87    8.07   \n",
       "2023-11-14   34.69   88.40  ...   17.62   14.92    3.00   11.84    8.08   \n",
       "2023-11-15   35.24   89.01  ...   17.76   15.53    3.01   11.98    8.14   \n",
       "\n",
       "            603920  601700  2378    2088    300755  \n",
       "2023-01-03   14.18    5.18    5.45   22.09   30.54  \n",
       "2023-01-04   13.99    5.22    5.38   21.49   29.68  \n",
       "2023-01-05   14.10    5.30    5.38   22.23   30.99  \n",
       "2023-01-06   14.25    5.54    5.40   22.17   30.17  \n",
       "2023-01-09   14.33    5.43    5.51   22.46   31.22  \n",
       "...            ...     ...     ...     ...     ...  \n",
       "2023-11-09   17.86    4.90    5.66   15.53   19.77  \n",
       "2023-11-10   17.86    4.94    5.63   15.50   19.64  \n",
       "2023-11-13   18.03    4.97    5.72   15.41   19.50  \n",
       "2023-11-14   18.33    5.03    5.71   15.42   19.53  \n",
       "2023-11-15   18.50    5.03    5.80   15.55   19.67  \n",
       "\n",
       "[210 rows x 36 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00,  1.04it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[603288, 0.0483563616269006],\n",
       " [300015, 0.07378464940113848],\n",
       " [603288, 0.0483563616269006],\n",
       " [300146, 0.03771160972847492],\n",
       " [600556, -0.0168252798832999],\n",
       " [300741, 0.11558793759968021],\n",
       " [600363, -0.08903689198459638],\n",
       " [301047, 0.03366876192737906],\n",
       " [300158, 0.12526757641615988],\n",
       " [2741, 0.11365474308705258],\n",
       " [688, -0.08292937208196274],\n",
       " [2759, 0.060439878466123996],\n",
       " [2461, -0.045063170269909476],\n",
       " [603920, -0.0995736122544276]]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "index_one = qs.get_data('513130',start='20230101')['close']\n",
    "id = []\n",
    "p = []\n",
    "for i in range(len(xggp)):\n",
    "    stat,p = ss.pearsonr(index_one, all_df.iloc[:,i])\n",
    "    if p > 0.05:\n",
    "        id.append([xggp[i],stat])\n",
    "id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_id = pd.DataFrame(id,columns=['id','stat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14/14 [00:01<00:00,  9.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                海天味业       天下秀      光华科技      振东制药      联创光电      汤臣倍健  \\\n",
      "2023-01-04 -0.009040 -0.004243  0.000540 -0.039062 -0.016168  0.024423   \n",
      "2023-01-05  0.038849  0.000000  0.003240 -0.008130  0.038209  0.020373   \n",
      "2023-01-06 -0.008327 -0.028409  0.009150 -0.019126 -0.005936  0.031436   \n",
      "2023-01-09  0.011756 -0.004386 -0.009067  0.002786 -0.009554  0.000000   \n",
      "2023-01-10 -0.000905 -0.002937  0.045210 -0.013889 -0.009646 -0.001647   \n",
      "...              ...       ...       ...       ...       ...       ...   \n",
      "2023-11-09  0.012888 -0.014019 -0.004110 -0.010989  0.000278 -0.001638   \n",
      "2023-11-10 -0.003895 -0.023697  0.004127  0.007937 -0.003055 -0.004923   \n",
      "2023-11-13 -0.002868  0.024272  0.010959  0.011024  0.000000 -0.005498   \n",
      "2023-11-14  0.002614  0.006319  0.010840  0.015576 -0.003900  0.001106   \n",
      "2023-11-15  0.020860 -0.006279  0.040885  0.010736 -0.017897  0.009387   \n",
      "\n",
      "                华宝股份      海天味业      义翘神州      爱尔眼科      天际股份      世运电路  \\\n",
      "2023-01-04 -0.012516 -0.009040  0.061474  0.006416 -0.011671 -0.013399   \n",
      "2023-01-05  0.005682  0.038849 -0.005848  0.019550  0.024239  0.007863   \n",
      "2023-01-06 -0.001738 -0.008327 -0.014611 -0.015840  0.010922  0.010638   \n",
      "2023-01-09  0.004354  0.011756 -0.007510  0.000000  0.002401  0.005614   \n",
      "2023-01-10 -0.003901 -0.000905  0.001067  0.036849  0.000599  0.005583   \n",
      "...              ...       ...       ...       ...       ...       ...   \n",
      "2023-11-09 -0.011256  0.012888 -0.011012 -0.007159 -0.000841 -0.006674   \n",
      "2023-11-10  0.001821 -0.003895 -0.000334 -0.017748 -0.004209  0.000000   \n",
      "2023-11-13  0.006818 -0.002868 -0.002228 -0.007905  0.003381  0.009518   \n",
      "2023-11-14  0.001806  0.002614 -0.003014  0.003984 -0.002527  0.016639   \n",
      "2023-11-15  0.008112  0.020860  0.000224 -0.000567  0.011824  0.009274   \n",
      "\n",
      "                珠江啤酒      上海石化  \n",
      "2023-01-04  0.001295  0.003185  \n",
      "2023-01-05  0.025873  0.003175  \n",
      "2023-01-06 -0.027743 -0.003165  \n",
      "2023-01-09  0.015564  0.000000  \n",
      "2023-01-10  0.002554 -0.015873  \n",
      "...              ...       ...  \n",
      "2023-11-09  0.001224  0.000000  \n",
      "2023-11-10 -0.015892  0.003367  \n",
      "2023-11-13  0.002484  0.016779  \n",
      "2023-11-14  0.001239 -0.009901  \n",
      "2023-11-15  0.007426  0.003333  \n",
      "\n",
      "[209 rows x 14 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\第四题.ipynb Cell 24\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/OneDrive/%E6%A1%8C%E9%9D%A2/A%E9%87%91%E8%9E%8D%E5%A4%A7%E6%B9%BE%E5%8C%BA/%E7%AC%AC%E5%9B%9B%E9%A2%98.ipynb#X45sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m# rets.to_excel(r'c:\\Users\\Lenovo\\OneDrive\\桌面\\相关股票收益率.xlsx')\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/OneDrive/%E6%A1%8C%E9%9D%A2/A%E9%87%91%E8%9E%8D%E5%A4%A7%E6%B9%BE%E5%8C%BA/%E7%AC%AC%E5%9B%9B%E9%A2%98.ipynb#X45sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mprint\u001b[39m(rets)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/OneDrive/%E6%A1%8C%E9%9D%A2/A%E9%87%91%E8%9E%8D%E5%A4%A7%E6%B9%BE%E5%8C%BA/%E7%AC%AC%E5%9B%9B%E9%A2%98.ipynb#X45sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m plot\u001b[39m.\u001b[39;49mline(rets)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\qstock\\plot\\data_plot.py:39\u001b[0m, in \u001b[0;36mline\u001b[1;34m(data, x, y, title, color, line_group, facet_col, legend, notebook)\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mline\u001b[39m(data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, x\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, title\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, color\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, line_group\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \n\u001b[0;32m     35\u001b[0m          facet_col\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, legend\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,notebook\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[0;32m     36\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"画折线图\u001b[39;00m\n\u001b[0;32m     37\u001b[0m \u001b[39m    data为series或dataframe的时候，可以不输入x和y\u001b[39;00m\n\u001b[0;32m     38\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 39\u001b[0m     fig \u001b[39m=\u001b[39m px\u001b[39m.\u001b[39;49mline(data_frame\u001b[39m=\u001b[39;49mdata, x\u001b[39m=\u001b[39;49mx, y\u001b[39m=\u001b[39;49my, title\u001b[39m=\u001b[39;49mtitle, \\\n\u001b[0;32m     40\u001b[0m                   color\u001b[39m=\u001b[39;49mcolor, line_group\u001b[39m=\u001b[39;49mline_group, facet_col\u001b[39m=\u001b[39;49mfacet_col)\n\u001b[0;32m     41\u001b[0m     fig\u001b[39m.\u001b[39mupdate_layout(showlegend\u001b[39m=\u001b[39mlegend)\n\u001b[0;32m     42\u001b[0m     fig\u001b[39m.\u001b[39mupdate_layout(\n\u001b[0;32m     43\u001b[0m         title\u001b[39m=\u001b[39m{\n\u001b[0;32m     44\u001b[0m             \u001b[39m'\u001b[39m\u001b[39mtext\u001b[39m\u001b[39m'\u001b[39m: title,  \u001b[39m# 标题名称\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[39m'\u001b[39m\u001b[39mxanchor\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mcenter\u001b[39m\u001b[39m'\u001b[39m,  \u001b[39m# 相对位置\u001b[39;00m\n\u001b[0;32m     48\u001b[0m             \u001b[39m'\u001b[39m\u001b[39myanchor\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mtop\u001b[39m\u001b[39m'\u001b[39m})\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\plotly\\express\\_chart_types.py:264\u001b[0m, in \u001b[0;36mline\u001b[1;34m(data_frame, x, y, line_group, color, line_dash, symbol, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, line_dash_sequence, line_dash_map, symbol_sequence, symbol_map, markers, log_x, log_y, range_x, range_y, line_shape, render_mode, title, template, width, height)\u001b[0m\n\u001b[0;32m    216\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mline\u001b[39m(\n\u001b[0;32m    217\u001b[0m     data_frame\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    218\u001b[0m     x\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    258\u001b[0m     height\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    259\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m go\u001b[39m.\u001b[39mFigure:\n\u001b[0;32m    260\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    261\u001b[0m \u001b[39m    In a 2D line plot, each row of `data_frame` is represented as vertex of\u001b[39;00m\n\u001b[0;32m    262\u001b[0m \u001b[39m    a polyline mark in 2D space.\u001b[39;00m\n\u001b[0;32m    263\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 264\u001b[0m     \u001b[39mreturn\u001b[39;00m make_figure(args\u001b[39m=\u001b[39;49m\u001b[39mlocals\u001b[39;49m(), constructor\u001b[39m=\u001b[39;49mgo\u001b[39m.\u001b[39;49mScatter)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\plotly\\express\\_core.py:2076\u001b[0m, in \u001b[0;36mmake_figure\u001b[1;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[0;32m   2073\u001b[0m layout_patch \u001b[39m=\u001b[39m layout_patch \u001b[39mor\u001b[39;00m {}\n\u001b[0;32m   2074\u001b[0m apply_default_cascade(args)\n\u001b[1;32m-> 2076\u001b[0m args \u001b[39m=\u001b[39m build_dataframe(args, constructor)\n\u001b[0;32m   2077\u001b[0m \u001b[39mif\u001b[39;00m constructor \u001b[39min\u001b[39;00m [go\u001b[39m.\u001b[39mTreemap, go\u001b[39m.\u001b[39mSunburst, go\u001b[39m.\u001b[39mIcicle] \u001b[39mand\u001b[39;00m args[\u001b[39m\"\u001b[39m\u001b[39mpath\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   2078\u001b[0m     args \u001b[39m=\u001b[39m process_dataframe_hierarchy(args)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\plotly\\express\\_core.py:1485\u001b[0m, in \u001b[0;36mbuild_dataframe\u001b[1;34m(args, constructor)\u001b[0m\n\u001b[0;32m   1482\u001b[0m     args[\u001b[39m\"\u001b[39m\u001b[39mcolor\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   1483\u001b[0m \u001b[39m# now that things have been prepped, we do the systematic rewriting of `args`\u001b[39;00m\n\u001b[1;32m-> 1485\u001b[0m df_output, wide_id_vars \u001b[39m=\u001b[39m process_args_into_dataframe(\n\u001b[0;32m   1486\u001b[0m     args, wide_mode, var_name, value_name\n\u001b[0;32m   1487\u001b[0m )\n\u001b[0;32m   1489\u001b[0m \u001b[39m# now that `df_output` exists and `args` contains only references, we complete\u001b[39;00m\n\u001b[0;32m   1490\u001b[0m \u001b[39m# the special-case and wide-mode handling by further rewriting args and/or mutating\u001b[39;00m\n\u001b[0;32m   1491\u001b[0m \u001b[39m# df_output\u001b[39;00m\n\u001b[0;32m   1493\u001b[0m count_name \u001b[39m=\u001b[39m _escape_col_name(df_output, \u001b[39m\"\u001b[39m\u001b[39mcount\u001b[39m\u001b[39m\"\u001b[39m, [var_name, value_name])\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\plotly\\express\\_core.py:1225\u001b[0m, in \u001b[0;36mprocess_args_into_dataframe\u001b[1;34m(args, wide_mode, var_name, value_name)\u001b[0m\n\u001b[0;32m   1223\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1224\u001b[0m         col_name \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(argument)\n\u001b[1;32m-> 1225\u001b[0m         df_output[col_name] \u001b[39m=\u001b[39m to_unindexed_series(\n\u001b[0;32m   1226\u001b[0m             df_input[argument], col_name\n\u001b[0;32m   1227\u001b[0m         )\n\u001b[0;32m   1228\u001b[0m \u001b[39m# ----------------- argument is likely a column / array / list.... -------\u001b[39;00m\n\u001b[0;32m   1229\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1230\u001b[0m     \u001b[39mif\u001b[39;00m df_provided \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(argument, \u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m):\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\plotly\\express\\_core.py:1073\u001b[0m, in \u001b[0;36mto_unindexed_series\u001b[1;34m(x, name)\u001b[0m\n\u001b[0;32m   1066\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mto_unindexed_series\u001b[39m(x, name\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m   1067\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   1068\u001b[0m \u001b[39m    assuming x is list-like or even an existing pd.Series, return a new pd.Series with\u001b[39;00m\n\u001b[0;32m   1069\u001b[0m \u001b[39m    no index, without extracting the data from an existing Series via numpy, which\u001b[39;00m\n\u001b[0;32m   1070\u001b[0m \u001b[39m    seems to mangle datetime columns. Stripping the index from existing pd.Series is\u001b[39;00m\n\u001b[0;32m   1071\u001b[0m \u001b[39m    required to get things to match up right in the new DataFrame we're building\u001b[39;00m\n\u001b[0;32m   1072\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1073\u001b[0m     \u001b[39mreturn\u001b[39;00m pd\u001b[39m.\u001b[39;49mSeries(x, name\u001b[39m=\u001b[39;49mname)\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32me:\\python11\\venv\\Lib\\site-packages\\pandas\\core\\series.py:386\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    382\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    384\u001b[0m     name \u001b[39m=\u001b[39m ibase\u001b[39m.\u001b[39mmaybe_extract_name(name, data, \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m))\n\u001b[1;32m--> 386\u001b[0m     \u001b[39mif\u001b[39;00m is_empty_data(data) \u001b[39mand\u001b[39;00m dtype \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    387\u001b[0m         \u001b[39m# gh-17261\u001b[39;00m\n\u001b[0;32m    388\u001b[0m         warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    389\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mThe default dtype for empty Series will be \u001b[39m\u001b[39m'\u001b[39m\u001b[39mobject\u001b[39m\u001b[39m'\u001b[39m\u001b[39m instead \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    390\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mof \u001b[39m\u001b[39m'\u001b[39m\u001b[39mfloat64\u001b[39m\u001b[39m'\u001b[39m\u001b[39m in a future version. Specify a dtype explicitly \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    393\u001b[0m             stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    394\u001b[0m         )\n\u001b[0;32m    395\u001b[0m         \u001b[39m# uncomment the line below when removing the FutureWarning\u001b[39;00m\n\u001b[0;32m    396\u001b[0m         \u001b[39m# dtype = np.dtype(object)\u001b[39;00m\n",
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      "\u001b[1;31mValueError\u001b[0m: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "global_indexs=data_id.iloc[:,0].tolist()\n",
    "index_data=qs.get_price(global_indexs,start='2023-01-01').dropna()\n",
    "# data=index_data.copy().dropna()\n",
    "rets=data/data.shift(1)-1\n",
    "rets=rets.to_period('D')\n",
    "# print(rets)\n",
    "# rets=(rets.groupby(rets.index).apply(lambda x: ((1+x).cumprod()-1).iloc[-1])*100).round(2)\n",
    "# rets=rets.iloc[-1].sort_values(ascending=False)\n",
    "title='相关股票2023年收益率曲线'\n",
    "rets.index = rets.index.to_numpy(dtype=str).tolist()\n",
    "rets = rets.dropna()\n",
    "# rets.to_excel(r'c:\\Users\\Lenovo\\OneDrive\\桌面\\相关股票收益率.xlsx')\n",
    "print(rets)\n",
    "plot.line(rets[['珠江啤酒', '上海石化']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
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    {
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     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.20s/it]\n"
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    },
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           "showlakes": true,
           "showland": true,
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          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          },
          "scene": {
           "xaxis": {
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            "gridwidth": 2,
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           "bgcolor": "#E5ECF6",
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            "gridcolor": "white",
            "linecolor": "white",
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
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           "ticks": "",
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           "title": {
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           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "恒生科技ETF收益率",
         "x": 0.5,
         "xanchor": "center",
         "y": 0.9,
         "yanchor": "top"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "index"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "value"
         }
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       }
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     },
     "metadata": {},
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   ],
   "source": [
    "#常见的全球指数名称\n",
    "index_data=qs.get_price('513130',start='2023-10-01',freq=5).dropna()\n",
    "data=index_data.copy().dropna()\n",
    "rets=data/data.shift(1)-1\n",
    "rets=rets.to_period('D')\n",
    "rets=(rets.groupby(rets.index).apply(lambda x: ((1+x).cumprod()-1).iloc[-1])*100).round(2)\n",
    "# rets=rets.iloc[-1].sort_values(ascending=False)\n",
    "import json\n",
    "title='相关股票2023年收益率曲线'\n",
    "# rets.index = rets.index.\n",
    "# to_numpy(dtype=str).tolist()\n",
    "# rets.dropna().to_excel(r'c:\\Users\\Lenovo\\OneDrive\\桌面\\相关股票收益率.xlsx')\n",
    "plot.chart_bar(rets['恒生科技ETF'])\n",
    "rets_copy = rets.copy()\n",
    "rets_copy.index = rets.index.to_numpy(dtype=str).tolist()\n",
    "plot.line(rets_copy['恒生科技ETF'],title=f'恒生科技ETF收益率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'index_one' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Lenovo\\OneDrive\\桌面\\A金融大湾区\\第四题.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Lenovo/OneDrive/%E6%A1%8C%E9%9D%A2/A%E9%87%91%E8%9E%8D%E5%A4%A7%E6%B9%BE%E5%8C%BA/%E7%AC%AC%E5%9B%9B%E9%A2%98.ipynb#X32sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plot\u001b[39m.\u001b[39mstock_line(index_one)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'index_one' is not defined"
     ]
    }
   ],
   "source": [
    "plot.stock_line(index_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['name', 'code', 'open', 'high', 'low', 'close', 'volume', 'turnover',\n",
      "       'turnover_rate'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\Lenovo\\\\OneDrive\\\\桌面\\\\kk.html'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_tow = qs.get_data('513130',start='20231109',fqt=0,freq=5)\n",
    "print(index_tow.columns)\n",
    "try:\n",
    "    index_tow.columns = ['name', 'code', 'open', 'high', 'low', 'close', 'volume', 'turnover']\n",
    "except:\n",
    "    index_tow.columns = ['name', 'code', 'open', 'high', 'low', 'close', 'volume', 'turnover','turnover_rate']\n",
    "plot.kline(index_tow).render(r'c:\\Users\\Lenovo\\OneDrive\\桌面\\kk.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c:\\\\Users\\\\Lenovo\\\\OneDrive\\\\桌面\\\\gg.html'"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.HA_kline(index_tow).render(r'c:\\Users\\Lenovo\\OneDrive\\桌面\\gg.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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